WorldWideScience

Sample records for strong predictive power

  1. Strong ground motion prediction using virtual earthquakes.

    Science.gov (United States)

    Denolle, M A; Dunham, E M; Prieto, G A; Beroza, G C

    2014-01-24

    Sedimentary basins increase the damaging effects of earthquakes by trapping and amplifying seismic waves. Simulations of seismic wave propagation in sedimentary basins capture this effect; however, there exists no method to validate these results for earthquakes that have not yet occurred. We present a new approach for ground motion prediction that uses the ambient seismic field. We apply our method to a suite of magnitude 7 scenario earthquakes on the southern San Andreas fault and compare our ground motion predictions with simulations. Both methods find strong amplification and coupling of source and structure effects, but they predict substantially different shaking patterns across the Los Angeles Basin. The virtual earthquake approach provides a new approach for predicting long-period strong ground motion.

  2. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  3. Morphological Atherosclerosis Calcification Distribution (MACD) Index is a Strong Predictor of Cardio-Vascular Death and Include Predictive Power of BMD

    DEFF Research Database (Denmark)

    Christiansen, Claus; Karsdal, Morten; Ganz, Melanie

    Aortic calcification is a major risk factor for cardiovascular disease (CVD) related deaths. We investigated the relation between mortality and aspects of number, size, morphology and distribution of calcified plaques in the lumbar aorta and BMD of postmenopausal women. 308 women aged 48 to 76 were...... was significantly higher than AC24 and any single or multivariate metabolic/physical marker. BMD correlates with AC24 among CVD dead patients (p=0.03) unlike MACD (p=0.43). The recent MACD-index provides a unique combination of morphology and distribution of aortic calcifications, factors that in a combination...... increase the biological relevance of the index by emphasizing that smaller plaques with a spread elongated morphology have a larger growth potential and thereby subsequent rupture potential. It includes the predictive power of BMD unlike the AC24 index. Thereby, in the current cohort with a long term...

  4. Strong earthquakes can be predicted: a multidisciplinary method for strong earthquake prediction

    Directory of Open Access Journals (Sweden)

    J. Z. Li

    2003-01-01

    Full Text Available The imminent prediction on a group of strong earthquakes that occurred in Xinjiang, China in April 1997 is introduced in detail. The prediction was made on the basis of comprehensive analyses on the results obtained by multiple innovative methods including measurements of crustal stress, observation of infrasonic wave in an ultra low frequency range, and recording of abnormal behavior of certain animals. Other successful examples of prediction are also enumerated. The statistics shows that above 40% of 20 total predictions jointly presented by J. Z. Li, Z. Q. Ren and others since 1995 can be regarded as effective. With the above methods, precursors of almost every strong earthquake around the world that occurred in recent years were recorded in our laboratory. However, the physical mechanisms of the observed precursors are yet impossible to explain at this stage.

  5. Is It Possible to Predict Strong Earthquakes?

    Science.gov (United States)

    Polyakov, Y. S.; Ryabinin, G. V.; Solovyeva, A. B.; Timashev, S. F.

    2015-07-01

    The possibility of earthquake prediction is one of the key open questions in modern geophysics. We propose an approach based on the analysis of common short-term candidate precursors (2 weeks to 3 months prior to strong earthquake) with the subsequent processing of brain activity signals generated in specific types of rats (kept in laboratory settings) who reportedly sense an impending earthquake a few days prior to the event. We illustrate the identification of short-term precursors using the groundwater sodium-ion concentration data in the time frame from 2010 to 2014 (a major earthquake occurred on 28 February 2013) recorded at two different sites in the southeastern part of the Kamchatka Peninsula, Russia. The candidate precursors are observed as synchronized peaks in the nonstationarity factors, introduced within the flicker-noise spectroscopy framework for signal processing, for the high-frequency component of both time series. These peaks correspond to the local reorganizations of the underlying geophysical system that are believed to precede strong earthquakes. The rodent brain activity signals are selected as potential "immediate" (up to 2 weeks) deterministic precursors because of the recent scientific reports confirming that rodents sense imminent earthquakes and the population-genetic model of K irshvink (Soc Am 90, 312-323, 2000) showing how a reliable genetic seismic escape response system may have developed over the period of several hundred million years in certain animals. The use of brain activity signals, such as electroencephalograms, in contrast to conventional abnormal animal behavior observations, enables one to apply the standard "input-sensor-response" approach to determine what input signals trigger specific seismic escape brain activity responses.

  6. Prediction of the occurrence of related strong earthquakes in Italy

    International Nuclear Information System (INIS)

    Vorobieva, I.A.; Panza, G.F.

    1993-06-01

    In the seismic flow it is often observed that a Strong Earthquake (SE), is followed by Related Strong Earthquakes (RSEs), which occur near the epicentre of the SE with origin time rather close to the origin time of the SE. The algorithm for the prediction of the occurrence of a RSE has been developed and applied for the first time to the seismicity data of the California-Nevada region and has been successfully tested in several regions of the World, the statistical significance of the result being 97%. So far, it has been possible to make five successful forward predictions, with no false alarms or failures to predict. The algorithm is applied here to the Italian territory, where the occurrence of RSEs is a particularly rare phenomenon. Our results show that the standard algorithm is successfully directly applicable without any adjustment of the parameters. Eleven SEs are considered. Of them, three are followed by a RSE, as predicted by the algorithm, eight SEs are not followed by a RSE, and the algorithm predicts this behaviour for seven of them, giving rise to only one false alarm. Since, in Italy, quite often the series of strong earthquakes are relatively short, the algorithm has been extended to handle such situation. The result of this experiment indicates that it is possible to attempt to test a SE, for the occurrence of a RSE, soon after the occurrence of the SE itself, performing timely ''preliminary'' recognition on reduced data sets. This fact, the high confidence level of the retrospective analysis, and the first successful forward predictions, made in different parts of the World, indicates that, even if additional tests are desirable, the algorithm can already be considered for routine application to Civil Defence. (author). Refs, 3 figs, 7 tabs

  7. Power spectrum of dark matter substructure in strong gravitational lenses

    Science.gov (United States)

    Diaz Rivero, Ana; Cyr-Racine, Francis-Yan; Dvorkin, Cora

    2018-01-01

    Studying the smallest self-bound dark matter structure in our Universe can yield important clues about the fundamental particle nature of dark matter. Galaxy-scale strong gravitational lensing provides a unique way to detect and characterize dark matter substructures at cosmological distances from the Milky Way. Within the cold dark matter (CDM) paradigm, the number of low-mass subhalos within lens galaxies is expected to be large, implying that their contribution to the lensing convergence field is approximately Gaussian and could thus be described by their power spectrum. We develop here a general formalism to compute from first principles the substructure convergence power spectrum for different populations of dark matter subhalos. As an example, we apply our framework to two distinct subhalo populations: a truncated Navarro-Frenk-White subhalo population motivated by standard CDM, and a truncated cored subhalo population motivated by self-interacting dark matter (SIDM). We study in detail how the subhalo abundance, mass function, internal density profile, and concentration affect the amplitude and shape of the substructure power spectrum. We determine that the power spectrum is mostly sensitive to a specific combination of the subhalo abundance and moments of the mass function, as well as to the average tidal truncation scale of the largest subhalos included in the analysis. Interestingly, we show that the asymptotic slope of the substructure power spectrum at large wave number reflects the internal density profile of the subhalos. In particular, the SIDM power spectrum exhibits a characteristic steepening at large wave number absent in the CDM power spectrum, opening the possibility of using this observable, if at all measurable, to discern between these two scenarios.

  8. Prediction of strong earthquake motions on rock surface using evolutionary process models

    International Nuclear Information System (INIS)

    Kameda, H.; Sugito, M.

    1984-01-01

    Stochastic process models are developed for prediction of strong earthquake motions for engineering design purposes. Earthquake motions with nonstationary frequency content are modeled by using the concept of evolutionary processes. Discussion is focused on the earthquake motions on bed rocks which are important for construction of nuclear power plants in seismic regions. On this basis, two earthquake motion prediction models are developed, one (EMP-IB Model) for prediction with given magnitude and epicentral distance, and the other (EMP-IIB Model) to account for the successive fault ruptures and the site location relative to the fault of great earthquakes. (Author) [pt

  9. Strong Presidents, Robust Democracies? Separation of Powers and Rule of Law in Latin America

    Directory of Open Access Journals (Sweden)

    Marcus Melo

    2009-06-01

    Full Text Available The received wisdom on Latin America in the 1980s and 1990s was that countries where presidents enjoyed strong constitutional powers and where multiparty coalitions prevailed would be doomed to instability and institutional crises, while countries boasting weak presidents and strong parties were expected to consolidate democratic rule. After almost two decades, it is now widely acknowledged that this prediction failed. Recent re-conceptualizations of presidentalism have partly corrected the flaws in the established diagnosis but left unexplained the role of checks and balances and of the rule of law in containing presidential abuse and guaranteeing governability. The paper argues that the key to solving the paradox of strong presidents and robust democracies is that democratic stability in Latin American countries is a function of an extended system of checks and balances. These are ultimately generated by power fragmentation at the time of the constitutional choices over their institutional design and political competition sustaining their effective functioning.

  10. Staying Power of Churn Prediction Models

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Peter C.; Bijmolt, Tammo H. A.

    In this paper, we study the staying power of various churn prediction models. Staying power is defined as the predictive performance of a model in a number of periods after the estimation period. We examine two methods, logit models and classification trees, both with and without applying a bagging

  11. Freud, Adler, and Women: Powers of the "Weak" and "Strong."

    Science.gov (United States)

    DeVitis, Joseph L.

    1985-01-01

    This article discusses Freud's original psychoanalytic notions on women and morality and their influence on constructions of personality, power, culture, and socioeducational change. Also discussed is Freudian critic Alfred Adler's use of a larger external lens to focus women's lives in a wider context of "social interest" and social…

  12. Predictive power of nuclear-mass models

    Directory of Open Access Journals (Sweden)

    Yu. A. Litvinov

    2013-12-01

    Full Text Available Ten different theoretical models are tested for their predictive power in the description of nuclear masses. Two sets of experimental masses are used for the test: the older set of 2003 and the newer one of 2011. The predictive power is studied in two regions of nuclei: the global region (Z, N ≥ 8 and the heavy-nuclei region (Z ≥ 82, N ≥ 126. No clear correlation is found between the predictive power of a model and the accuracy of its description of the masses.

  13. Composable and Predictable Power Management

    NARCIS (Netherlands)

    Nelson, A.T.

    2014-01-01

    The functionality of embedded systems is ever growing. The computational power of embedded systems is growing to match this demand, with embedded multiprocessor systems becoming more common. The limitations of embedded systems are not always related to chip size but are commonly due to energy and/or

  14. Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

    Directory of Open Access Journals (Sweden)

    Yagang Zhang

    2015-01-01

    Full Text Available This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind speed predictions. Finally we separately use the original and improved wind speed series to predict the related wind power. This proves that the corrected wind speed provides higher precision wind power predictions. This research presents a totally new direction in the wind prediction field and has profound theoretical research value and practical guiding significance.

  15. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  16. Sound power flux measurements in strongly exited ducts with flow

    Science.gov (United States)

    Holland, Keith R.; Davies, Peter O. A. L.; van der Walt, Danie C.

    2002-12-01

    This contribution describes new robust procedures for the measurement of sound power flux at appropriate axial positions along a duct with flow, using pairs of flush wall mounted microphones, or pressure transducers. The technology includes the application of selective averaging, order tracking, and optimized sampling rate methods to identify the small fraction of the total fluctuating wave energy that is being propagated along the flow path in a reverberent, or highly reactive duct system. Such measurements can also be used to quantify the local acoustic characteristics that govern the generation, transfer, and propagation of wave energy in the system. Illustrative examples include the determination of the acoustic characteristics of individual silencing elements installed in IC engine intakes and exhausts both on the flow bench and during controlled acceleration or run down on a test bed, where the wave component spectral levels approached 170 dB.

  17. Simulators predict power plant operation

    Energy Technology Data Exchange (ETDEWEB)

    Peltier, R.

    2002-07-01

    Mix the complexity of a new construction or major retrofit project with today's 'do more with less', a pinch of 'personnel inexperience,' and a dash of 'unintended consequences', and you have got a recipe for insomnia. Advanced simulation tools, however, can help you wring out your design train your operators before the first wire is terminated and just may be get a good night's rest. The article describes several examples of uses of simulation tools. Esscor recently completed a simulation project for a major US utility exploring the potential for furnace/duct implosion that could result from adding higher volumetric flow induced-draft fans and selective catalytic reduction to a 650-MW coal-fired plant. CAF Electronics Inc. provided a full-scope simulator for Alstom's KA24-1 combined-cycle power plant in Paris, France. Computational fluid dynamics (CFD) tools are being used by the Gas Technology Institute to simulate the performance of the next generation of pulverized coal combustors. 5 figs.

  18. Short-term wind power prediction

    DEFF Research Database (Denmark)

    Joensen, Alfred K.

    2003-01-01

    The present thesis consists of 10 research papers published during the period 1997-2002 together with a summary report. The objective of the work described in the thesis is to develop models and methods for calculation of high accuracy predictions of wind power generated electricity, and to imple......The present thesis consists of 10 research papers published during the period 1997-2002 together with a summary report. The objective of the work described in the thesis is to develop models and methods for calculation of high accuracy predictions of wind power generated electricity......, and to implement these models and methods in an on-line software application. The economical value of having predictions available is also briefly considered. The summary report outlines the background and motivation for developing wind power prediction models. The meteorological theory which is relevant...

  19. Conditional prediction intervals of wind power generation

    DEFF Research Database (Denmark)

    Pinson, Pierre; Kariniotakis, Georges

    2010-01-01

    A generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform...... of the situation-specific uncertainty of point forecasts. In order to avoid a restrictive assumption on the shape of forecast error distributions, focus is given to an empirical and nonparametric approach named adapted resampling. This approach employs a fuzzy inference model that permits to integrate expertise...

  20. Optimal prediction intervals of wind power generation

    DEFF Research Database (Denmark)

    Wan, Can; Wu, Zhao; Pinson, Pierre

    2014-01-01

    direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been...... penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with Associated confidence levels are generated through...

  1. Using machine learning to predict wind turbine power output

    International Nuclear Information System (INIS)

    Clifton, A; Kilcher, L; Lundquist, J K; Fleming, P

    2013-01-01

    Wind turbine power output is known to be a strong function of wind speed, but is also affected by turbulence and shear. In this work, new aerostructural simulations of a generic 1.5 MW turbine are used to rank atmospheric influences on power output. Most significant is the hub height wind speed, followed by hub height turbulence intensity and then wind speed shear across the rotor disk. These simulation data are used to train regression trees that predict the turbine response for any combination of wind speed, turbulence intensity, and wind shear that might be expected at a turbine site. For a randomly selected atmospheric condition, the accuracy of the regression tree power predictions is three times higher than that from the traditional power curve methodology. The regression tree method can also be applied to turbine test data and used to predict turbine performance at a new site. No new data are required in comparison to the data that are usually collected for a wind resource assessment. Implementing the method requires turbine manufacturers to create a turbine regression tree model from test site data. Such an approach could significantly reduce bias in power predictions that arise because of the different turbulence and shear at the new site, compared to the test site. (letter)

  2. Predictive Trip Detection for Nuclear Power Plants

    Science.gov (United States)

    Rankin, Drew J.; Jiang, Jin

    2016-08-01

    This paper investigates the use of a Kalman filter (KF) to predict, within the shutdown system (SDS) of a nuclear power plant (NPP), whether safety parameter measurements have reached a trip set-point. In addition, least squares (LS) estimation compensates for prediction error due to system-model mismatch. The motivation behind predictive shutdown is to reduce the amount of time between the occurrence of a fault or failure and the time of trip detection, referred to as time-to-trip. These reductions in time-to-trip can ultimately lead to increases in safety and productivity margins. The proposed predictive SDS differs from conventional SDSs in that it compares point-predictions of the measurements, rather than sensor measurements, against trip set-points. The predictive SDS is validated through simulation and experiments for the steam generator water level safety parameter. Performance of the proposed predictive SDS is compared against benchmark conventional SDS with respect to time-to-trip. In addition, this paper analyzes: prediction uncertainty, as well as; the conditions under which it is possible to achieve reduced time-to-trip. Simulation results demonstrate that on average the predictive SDS reduces time-to-trip by an amount of time equal to the length of the prediction horizon and that the distribution of times-to-trip is approximately Gaussian. Experimental results reveal that a reduced time-to-trip can be achieved in a real-world system with unknown system-model mismatch and that the predictive SDS can be implemented with a scan time of under 100ms. Thus, this paper is a proof of concept for KF/LS-based predictive trip detection.

  3. Experimental demonstration of the equivalence of inductive and strongly coupled magnetic resonance wireless power transfer

    Science.gov (United States)

    Ricketts, David S.; Chabalko, Matthew J.; Hillenius, Andrew

    2013-02-01

    In this work, we show experimentally that wireless power transfer (WPT) using strongly coupled magnetic resonance (SCMR) and traditional induction are equivalent. We demonstrate that for a given coil separation, and to within 4%, strongly coupled magnetic resonance and traditional induction produce the same theoretical efficiency of wireless power transfer versus distance. Moreover, we show that the difference between traditional induction and strongly coupled magnetic resonance is in the implementation of the impedance matching network where strongly coupled magnetic resonance uses the mini-loop impedance match. The mini-loop impedance mach provides a low-loss, high-ratio impedance transformation that makes it desirable for longer distance wireless power transfer, where large impedance transformations are needed to maximize power transfer.

  4. Uncertainties in predicting solar panel power output

    Science.gov (United States)

    Anspaugh, B.

    1974-01-01

    The problem of calculating solar panel power output at launch and during a space mission is considered. The major sources of uncertainty and error in predicting the post launch electrical performance of the panel are considered. A general discussion of error analysis is given. Examples of uncertainty calculations are included. A general method of calculating the effect on the panel of various degrading environments is presented, with references supplied for specific methods. A technique for sizing a solar panel for a required mission power profile is developed.

  5. THRUST PREDICTION PROGRAM FOR MARINE JET POWER

    OpenAIRE

    Bergsek, Mattias

    2011-01-01

    Marine Jet Power, MJP wishes to investigate the possibility of transforming their current Thrust Prediction Program, TPP written in C++ source code into a more up to date tool for their sales staff. The old TPP, though an accurate and precise tool, is not documented and lacks commentaries in the source code. Therefore the beginning of this master thesis was about documenting and investigates what methods were used to calculate the performance of the water jet system.The next step was splittin...

  6. Wind Power Prediction Considering Nonlinear Atmospheric Disturbances

    OpenAIRE

    Yagang Zhang; Jingyun Yang; Kangcheng Wang; Zengping Wang

    2015-01-01

    This paper considers the effect of nonlinear atmospheric disturbances on wind power prediction. A Lorenz system is introduced as an atmospheric disturbance model. Three new improved wind forecasting models combined with a Lorenz comprehensive disturbance are put forward in this study. Firstly, we define the form of the Lorenz disturbance variable and the wind speed perturbation formula. Then, different artificial neural network models are used to verify the new idea and obtain better wind spe...

  7. Prediction of future dispute concerning nuclear power generation

    International Nuclear Information System (INIS)

    1981-04-01

    This investigation is the third research on the public acceptance of nuclear power generation by the National Congress on Social Economics. In this study, how the energy dispute including that concerning nuclear power generation will develop in 1980s and 1990s, how the form of dispute and the point of controversy will change, were predicted. Though the maintenance of the concord of groups strongly regulates the behavior of people, recently they have become to exercise individual rights frequently. The transition to the society of dispute is the natural result of the modernization of society and the increase of richness. The proper prediction of social problems and the planning and execution of proper countermeasures are very important. The background, objective, basic viewpoint, range and procedure of this investigation, the change of social dispute, the history of the dispute concerning nuclear power generation, the basic viewpoint in the prediction of the dispute concerning nuclear power generation, the social situation in 1980s, the prediction and avoidance of the dispute in view of social and energy situations, and the fundamental strategy for seeking a clue to the solution in 1980s and 1990s are described. The establishment of neutral mediation organs and the flexible technologies of nuclear reactors are necessary. (Kako, I.)

  8. Seismic rupture modelling, strong motion prediction and seismic hazard assessment: fundamental and applied approaches

    International Nuclear Information System (INIS)

    Berge-Thierry, C.

    2007-05-01

    The defence to obtain the 'Habilitation a Diriger des Recherches' is a synthesis of the research work performed since the end of my Ph D. thesis in 1997. This synthesis covers the two years as post doctoral researcher at the Bureau d'Evaluation des Risques Sismiques at the Institut de Protection (BERSSIN), and the seven consecutive years as seismologist and head of the BERSSIN team. This work and the research project are presented in the framework of the seismic risk topic, and particularly with respect to the seismic hazard assessment. Seismic risk combines seismic hazard and vulnerability. Vulnerability combines the strength of building structures and the human and economical consequences in case of structural failure. Seismic hazard is usually defined in terms of plausible seismic motion (soil acceleration or velocity) in a site for a given time period. Either for the regulatory context or the structural specificity (conventional structure or high risk construction), seismic hazard assessment needs: to identify and locate the seismic sources (zones or faults), to characterize their activity, to evaluate the seismic motion to which the structure has to resist (including the site effects). I specialized in the field of numerical strong-motion prediction using high frequency seismic sources modelling and forming part of the IRSN allowed me to rapidly working on the different tasks of seismic hazard assessment. Thanks to the expertise practice and the participation to the regulation evolution (nuclear power plants, conventional and chemical structures), I have been able to work on empirical strong-motion prediction, including site effects. Specific questions related to the interface between seismologists and structural engineers are also presented, especially the quantification of uncertainties. This is part of the research work initiated to improve the selection of the input ground motion in designing or verifying the stability of structures. (author)

  9. Contribution of Strong Discontinuities to the Power Spectrum of the Solar Wind

    International Nuclear Information System (INIS)

    Borovsky, Joseph E.

    2010-01-01

    Eight and a half years of magnetic field measurements (2 22 samples) from the ACE spacecraft in the solar wind at 1 A.U. are analyzed. Strong (large-rotation-angle) discontinuities in the solar wind are collected and measured. An artificial time series is created that preserves the timing and amplitudes of the discontinuities. The power spectral density of the discontinuity series is calculated and compared with the power spectral density of the solar-wind magnetic field. The strong discontinuities produce a power-law spectrum in the ''inertial subrange'' with a spectral index near the Kolmogorov -5/3 index. The discontinuity spectrum contains about half of the power of the full solar-wind magnetic field over this ''inertial subrange.'' Warnings are issued about the significant contribution of discontinuities to the spectrum of the solar wind, complicating interpretation of spectral power and spectral indices.

  10. Contribution of strong discontinuities to the power spectrum of the solar wind.

    Science.gov (United States)

    Borovsky, Joseph E

    2010-09-10

    Eight and a half years of magnetic field measurements (2(22) samples) from the ACE spacecraft in the solar wind at 1 A.U. are analyzed. Strong (large-rotation-angle) discontinuities in the solar wind are collected and measured. An artificial time series is created that preserves the timing and amplitudes of the discontinuities. The power spectral density of the discontinuity series is calculated and compared with the power spectral density of the solar-wind magnetic field. The strong discontinuities produce a power-law spectrum in the "inertial subrange" with a spectral index near the Kolmogorov -5/3 index. The discontinuity spectrum contains about half of the power of the full solar-wind magnetic field over this "inertial subrange." Warnings are issued about the significant contribution of discontinuities to the spectrum of the solar wind, complicating interpretation of spectral power and spectral indices.

  11. Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes

    Directory of Open Access Journals (Sweden)

    Sakanyan Vehary

    2008-05-01

    Full Text Available Abstract Background Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes. Results We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group I σ70 factors of Escherichia coli RNA polymerase. It detects promoter-specific motifs by consecutively matching three patterns, consisting of an UP-element, required for interaction with the α subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the σ70 subunit of RNA polymerase. Analysis of 43 bacterial genomes revealed that the frequency of candidate sequences depends on the A+T content of the DNA under examination. The accuracy of in silico prediction was experimentally validated for the genome of a hyperthermophilic bacterium, Thermotoga maritima, by applying a cell-free expression assay using the predicted strong promoters. In this organism, the strong promoters govern genes for translation, energy metabolism, transport, cell movement, and other as-yet unidentified functions. Conclusion The triad pattern algorithm developed for predicting strong bacterial promoters is well suited for analyzing bacterial genomes with an A+T content of less than 62%. This computational tool opens new prospects for investigating global gene expression, and individual strong promoters in bacteria of medical and/or economic significance.

  12. Using Predictive Analytics to Predict Power Outages from Severe Weather

    Science.gov (United States)

    Wanik, D. W.; Anagnostou, E. N.; Hartman, B.; Frediani, M. E.; Astitha, M.

    2015-12-01

    The distribution of reliable power is essential to businesses, public services, and our daily lives. With the growing abundance of data being collected and created by industry (i.e. outage data), government agencies (i.e. land cover), and academia (i.e. weather forecasts), we can begin to tackle problems that previously seemed too complex to solve. In this session, we will present newly developed tools to aid decision-support challenges at electric distribution utilities that must mitigate, prepare for, respond to and recover from severe weather. We will show a performance evaluation of outage predictive models built for Eversource Energy (formerly Connecticut Light & Power) for storms of all types (i.e. blizzards, thunderstorms and hurricanes) and magnitudes (from 20 to >15,000 outages). High resolution weather simulations (simulated with the Weather and Research Forecast Model) were joined with utility outage data to calibrate four types of models: a decision tree (DT), random forest (RF), boosted gradient tree (BT) and an ensemble (ENS) decision tree regression that combined predictions from DT, RF and BT. The study shows that the ENS model forced with weather, infrastructure and land cover data was superior to the other models we evaluated, especially in terms of predicting the spatial distribution of outages. This research has the potential to be used for other critical infrastructure systems (such as telecommunications, drinking water and gas distribution networks), and can be readily expanded to the entire New England region to facilitate better planning and coordination among decision-makers when severe weather strikes.

  13. Strong enhancement of streaming current power by application of two phase flow

    NARCIS (Netherlands)

    Xie, Yanbo; Sherwood, John D.; Shui, Lingling; van den Berg, Albert; Eijkel, Jan C.T.

    2011-01-01

    We show that the performance of a streaming-potential based microfluidic energy conversion system can be strongly en-hanced by the use of two phase flow. In single-phase systems, the internal conduction current induced by the streaming poten-tial limits the output power, while in a two-phase system

  14. Fast-Projectile Stopping Power of Quantal Multicomponent Strongly Coupled Plasmas

    International Nuclear Information System (INIS)

    Ballester, D.; Tkachenko, I. M.

    2008-01-01

    The Bethe-Larkin formula for the fast-projectile stopping power is extended to multicomponent plasmas. The results are to contribute to the correct interpretation of the experimental data, which could permit us to test existing and future models of thermodynamic, static, and dynamic characteristics of strongly coupled Coulomb systems

  15. Investigation on the dynamic behaviour of a parabolic trough power plant during strongly cloudy days

    International Nuclear Information System (INIS)

    Al-Maliki, Wisam Abed Kattea; Alobaid, Falah; Starkloff, Ralf; Kez, Vitali; Epple, Bernd

    2016-01-01

    Highlights: • A detailed dynamic model of a parabolic trough solar thermal power plant is done. • Simulated results are compared to the experimental data from the real power plant. • Discrepancy between model result and real data is caused by operation strategy. • The model strategy increased the operating hours of power plant by around 2.5–3 h. - Abstract: The objective of this study is the development of a full scale dynamic model of a parabolic trough power plant with a thermal storage system, operated by the Actividades de Construcción y Servicios Group in Spain. The model includes solar field, thermal storage system and the power block and describes the heat transfer fluid and steam/water paths in detail. The parabolic trough power plant is modelled using Advanced Process Simulation Software (APROS). To validate the model, the numerical results are compared to the measured data, obtained from “Andasol II” during strongly cloudy periods in the summer days. The comparisons show a qualitative agreement between the dynamic simulation model and the measurements. The results confirm that the thermal storage enables the parabolic trough power plant to provide a constant power rate when the storage energy discharge is available, despite significant oscillations in the solar radiation.

  16. Prediction and discovery of extremely strong hydrodynamic instabilities due to a velocity jump: theory and experiments

    International Nuclear Information System (INIS)

    Fridman, A M

    2008-01-01

    The theory and the experimental discovery of extremely strong hydrodynamic instabilities are described, viz. the Kelvin-Helmholtz, centrifugal, and superreflection instabilities. The discovery of the last two instabilities was predicted and the Kelvin-Helmholtz instability in real systems was revised by us. (reviews of topical problems)

  17. What Factors Predict Who Will Have a Strong Social Network Following a Stroke?

    Science.gov (United States)

    Northcott, Sarah; Marshall, Jane; Hilari, Katerina

    2016-01-01

    Purpose: Measures of social networks assess the number and nature of a person's social contacts, and strongly predict health outcomes. We explored how social networks change following a stroke and analyzed concurrent and baseline predictors of social networks 6 months poststroke. Method: We conducted a prospective longitudinal observational study.…

  18. Prediction of strong ground motion based on scaling law of earthquake

    International Nuclear Information System (INIS)

    Kamae, Katsuhiro; Irikura, Kojiro; Fukuchi, Yasunaga.

    1991-01-01

    In order to predict more practically strong ground motion, it is important to study how to use a semi-empirical method in case of having no appropriate observation records for actual small-events as empirical Green's functions. We propose a prediction procedure using artificially simulated small ground motions as substitute for the actual motions. First, we simulate small-event motion by means of stochastic simulation method proposed by Boore (1983) in considering pass effects such as attenuation, and broadening of waveform envelope empirically in the objective region. Finally, we attempt to predict the strong ground motion due to a future large earthquake (M 7, Δ = 13 km) using the same summation procedure as the empirical Green's function method. We obtained the results that the characteristics of the synthetic motion using M 5 motion were in good agreement with those by the empirical Green's function method. (author)

  19. WHY WE CANNOT PREDICT STRONG EARTHQUAKES IN THE EARTH’S CRUST

    Directory of Open Access Journals (Sweden)

    Iosif L. Gufeld

    2011-01-01

    Full Text Available In the past decade, earthquake disasters caused multiple fatalities and significant economic losses and challenged the modern civilization. The wellknown achievements and growing power of civilization are backstrapped when facing the Nature. The question arises, what hinders solving a problem of earthquake prediction, while longterm and continuous seismic monitoring systems are in place in many regions of the world. For instance, there was no forecast of the Japan Great Earthquake of March 11, 2011, despite the fact that monitoring conditions for its prediction were unique. Its focal zone was 100–200 km away from the monitoring network installed in the area of permanent seismic hazard, which is subject to nonstop and longterm seismic monitoring. Lesson should be learned from our common fiasco in forecasting, taking into account research results obtained during the past 50–60 years. It is now evident that we failed to identify precursors of the earthquakes. Prior to the earthquake occurrence, the observed local anomalies of various fields reflected other processes that were mistakenly viewed as processes of preparation for largescale faulting. For many years, geotectonic situations were analyzed on the basis of the physics of destruction of laboratory specimens, which was applied to the lithospheric conditions. Many researchers realize that such an approach is inaccurate. Nonetheless, persistent attempts are being undertaken with application of modern computation to detect anomalies of various fields, which may be interpreted as earthquake precursors. In our opinion, such illusory intentions were smashed by the Great Japan Earthquake (Figure 6. It is also obvious that sufficient attention has not been given yet to fundamental studies of seismic processes.This review presents the authors’ opinion concerning the origin of the seismic process and strong earthquakes, being part of the process. The authors realize that a wide discussion is

  20. Risperidone and Venlafaxine Metabolic Ratios Strongly Predict a CYP2D6 Poor Metabolizing Genotype.

    Science.gov (United States)

    Mannheimer, Buster; Haslemo, Tore; Lindh, Jonatan D; Eliasson, Erik; Molden, Espen

    2016-02-01

    To investigate the predictive value of the risperidone and venlafaxine metabolic ratios and CYP2D6 genotype. The determination of risperidone, 9-hydroxyrisperidone, and venlafaxine, O-desmethylvenlafaxine, N-desmethylvenlafaxine and CYP2D6 genotype was performed in 425 and 491 patients, respectively. The receiver operator characteristic method and the area under the receiver operator characteristic curve were used to illustrate the predictive value of risperidone metabolic ratio for the individual CYP2D6 genotype. To evaluate the proposed cutoff levels of >1 to identify individuals with a poor CYP2D6 genotype, the sensitivity, specificity, positive predictive values, and negative predictive values were calculated. Area under the receiver operator characteristic curve to predict poor metabolizers for risperidone/9-hydroxyrisperidone and N-desmethylvenlafaxine/O-desmethylvenlafaxine ratios was 93% and 99%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value (confidence interval) of a risperidone/9-hydroxyrisperidone ratio >1 to predict a CYP2D6 poor metabolizer genotype were 91% (76%-97%), 86% (83%-89%), 35% (26%-46%), and 99% (97%-100%), respectively. The corresponding measures for N-desmethylvenlafaxine/O-desmethylvenlafaxine were 93% (76%-97%), 87% (83%-89%), 40% (32%-51%), and 99% (98%-100%). Risperidone/9-hydroxyrisperidone and N-desmethylvenlafaxine/O-desmethylvenlafaxine metabolic ratios >1 strongly predict individuals with poor metabolizer genotype, which could guide psychotropic drug treatment to avoid adverse drug reactions and to increase their therapeutic efficacy in patients prescribed these drugs.

  1. Research of Smart Payment System of Power Grid Using Strongly Sub-feasible SQP Algorithm

    Directory of Open Access Journals (Sweden)

    Yang Fang

    2017-01-01

    Full Text Available With the continuous development and perfection of “Internet + Electricity”, the regional grid operation has gradually realized the Internet-based automation. In order to improve the smart level of regional grid operation, this paper analyzes the status quo of power grid terminal in Fujian local power (group company, and introduces the strongly sub-feasible sequence quadratic programming (SQP. The smart payment system based on strongly sub-feasible SQP algorithm is described by its structure, function and implementation process. Through the information technology to improve the efficiency of the service, so that payment staff and smart terminal of self-service payment system has been information between the interactive mode, the actual operation effect is good.

  2. Monitoring of the future strong Vrancea events by using the CN formal earthquake prediction algorithm

    International Nuclear Information System (INIS)

    Moldoveanu, C.L.; Novikova, O.V.; Panza, G.F.; Radulian, M.

    2003-06-01

    The preparation process of the strong subcrustal events originating in Vrancea region, Romania, is monitored using an intermediate-term medium-range earthquake prediction method - the CN algorithm (Keilis-Borok and Rotwain, 1990). We present the results of the monitoring of the preparation of future strong earthquakes for the time interval from January 1, 1994 (1994.1.1), to January 1, 2003 (2003.1.1) using the updated catalogue of the Romanian local network. The database considered for the CN monitoring of the preparation of future strong earthquakes in Vrancea covers the period from 1966.3.1 to 2003.1.1 and the geographical rectangle 44.8 deg - 48.4 deg N, 25.0 deg - 28.0 deg E. The algorithm correctly identifies, by retrospective prediction, the TJPs for all the three strong earthquakes (Mo=6.4) that occurred in Vrancea during this period. The cumulated duration of the TIPs represents 26.5% of the total period of time considered (1966.3.1-2003.1.1). The monitoring of current seismicity using the algorithm CN has been carried out since 1994. No strong earthquakes occurred from 1994.1.1 to 2003.1.1 but the CN declared an extended false alarm from 1999.5.1 to 2000.11.1. No alarm has currently been declared in the region (on January 1, 2003), as can be seen from the TJPs diagram shown. (author)

  3. Enhanced outage prediction modeling for strong extratropical storms and hurricanes in the Northeastern United States

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Wanik, D. W.; Bhuiyan, M. A. E.; Zhang, X.; Yang, J.; Astitha, M.; Frediani, M. E.; Schwartz, C. S.; Pardakhti, M.

    2016-12-01

    The overwhelming majority of human activities need reliable electric power. Severe weather events can cause power outages, resulting in substantial economic losses and a temporary worsening of living conditions. Accurate prediction of these events and the communication of forecasted impacts to the affected utilities is necessary for efficient emergency preparedness and mitigation. The University of Connecticut Outage Prediction Model (OPM) uses regression tree models, high-resolution weather reanalysis and real-time weather forecasts (WRF and NCAR ensemble), airport station data, vegetation and electric grid characteristics and historical outage data to forecast the number and spatial distribution of outages in the power distribution grid located within dense vegetation. Recent OPM improvements consist of improved storm classification and addition of new predictive weather-related variables and are demonstrated using a leave-one-storm-out cross-validation based on 130 severe extratropical storms and two hurricanes (Sandy and Irene) in the Northeast US. We show that it is possible to predict the number of trouble spots causing outages in the electric grid with a median absolute percentage error as low as 27% for some storm types, and at most around 40%, in a scale that varies between four orders of magnitude, from few outages to tens of thousands. This outage information can be communicated to the electric utility to manage allocation of crews and equipment and minimize the recovery time for an upcoming storm hazard.

  4. Prediction of environmental impact of power generation

    International Nuclear Information System (INIS)

    1985-01-01

    The proceedings contain 12 papers of which 4 were inputted in INIS. The papers deal with the impact of the construction and operation of nuclear power plants on the environment, with radiation hazards due to gaseous emissions and liquid effluents from nuclear power plants, the design and power utilization of the water management system for the nuclear power plant being designed in southern Slovakia, and the limits of radiation protection of nuclear sources in Czechoslovakia. (E.S.)

  5. Strongly lensed gravitational waves and electromagnetic signals as powerful cosmic rulers

    Science.gov (United States)

    Wei, Jun-Jie; Wu, Xue-Feng

    2017-12-01

    In this paper, we discuss the possibility of using strongly lensed gravitational waves (GWs) and their electromagnetic (EM) counterparts as powerful cosmic rulers. In the EM domain, it has been suggested that joint observations of the time delay (Δτ) between lensed quasar images and the velocity dispersion (σ) of the lensing galaxy (i.e. the combination Δτ/σ2) are able to constrain the cosmological parameters more strongly than Δτ or σ2 separately. Here, for the first time, we propose that this Δτ/σ2 method can be applied to the strongly lensed systems observed in both GW and EM windows. Combining the redshifts, images and σ observed in the EM domain with the very precise Δτ derived from lensed GW signals, we expect that accurate multimessenger cosmology can be achieved in the era of third-generation GW detectors. Comparing with the constraints from the Δτ method, we prove that using Δτ/σ2 can improve the discrimination between cosmological models. Furthermore, we demonstrate that with ∼50 strongly lensed GW-EM systems, we can reach a constraint on the dark energy equation of state w comparable to the 580 Union2.1 Type Ia supernovae data. Much more stringent constraints on w can be obtained when combining the Δτ and Δτ/σ2 methods.

  6. The inner mass power spectrum of galaxies using strong gravitational lensing: beyond linear approximation

    Science.gov (United States)

    Chatterjee, Saikat; Koopmans, Léon V. E.

    2018-02-01

    In the last decade, the detection of individual massive dark matter sub-haloes has been possible using potential correction formalism in strong gravitational lens imaging. Here, we propose a statistical formalism to relate strong gravitational lens surface brightness anomalies to the lens potential fluctuations arising from dark matter distribution in the lens galaxy. We consider these fluctuations as a Gaussian random field in addition to the unperturbed smooth lens model. This is very similar to weak lensing formalism and we show that in this way we can measure the power spectrum of these perturbations to the potential. We test the method by applying it to simulated mock lenses of different geometries and by performing an MCMC analysis of the theoretical power spectra. This method can measure density fluctuations in early type galaxies on scales of 1-10 kpc at typical rms levels of a per cent, using a single lens system observed with the Hubble Space Telescope with typical signal-to-noise ratios obtained in a single orbit.

  7. Survey of strong motion earthquake effects on thermal power plants in California with emphasis on piping systems. Volume 2, Appendices

    Energy Technology Data Exchange (ETDEWEB)

    Stevenson, J.D. [Stevenson and Associates, Cleveland, OH (United States)

    1995-11-01

    Volume 2 of the ``Survey of Strong Motion Earthquake Effects on Thermal Power Plants in California with Emphasis on Piping Systems`` contains Appendices which detail the detail design and seismic response of several power plants subjected to strong motion earthquakes. The particular plants considered include the Ormond Beach, Long Beach and Seal Beach, Burbank, El Centro, Glendale, Humboldt Bay, Kem Valley, Pasadena and Valley power plants. Included is a typical power plant piping specification and photographs of typical power plant piping specification and photographs of typical piping and support installations for the plants surveyed. Detailed piping support spacing data are also included.

  8. CN earthquake prediction algorithm and the monitoring of the future strong Vrancea events

    International Nuclear Information System (INIS)

    Moldoveanu, C.L.; Radulian, M.; Novikova, O.V.; Panza, G.F.

    2002-01-01

    The strong earthquakes originating at intermediate-depth in the Vrancea region (located in the SE corner of the highly bent Carpathian arc) represent one of the most important natural disasters able to induce heavy effects (high tool of casualties and extensive damage) in the Romanian territory. The occurrence of these earthquakes is irregular, but not infrequent. Their effects are felt over a large territory, from Central Europe to Moscow and from Greece to Scandinavia. The largest cultural and economical center exposed to the seismic risk due to the Vrancea earthquakes is Bucharest. This metropolitan area (230 km 2 wide) is characterized by the presence of 2.5 million inhabitants (10% of the country population) and by a considerable number of high-risk structures and infrastructures. The best way to face strong earthquakes is to mitigate the seismic risk by using the two possible complementary approaches represented by (a) the antiseismic design of structures and infrastructures (able to support strong earthquakes without significant damage), and (b) the strong earthquake prediction (in terms of alarm intervals declared for long, intermediate or short-term space-and time-windows). The intermediate term medium-range earthquake prediction represents the most realistic target to be reached at the present state of knowledge. The alarm declared in this case extends over a time window of about one year or more, and a space window of a few hundreds of kilometers. In the case of Vrancea events the spatial uncertainty is much less, being of about 100 km. The main measures for the mitigation of the seismic risk allowed by the intermediate-term medium-range prediction are: (a) verification of the buildings and infrastructures stability and reinforcement measures when required, (b) elaboration of emergency plans of action, (c) schedule of the main actions required in order to restore the normality of the social and economical life after the earthquake. The paper presents the

  9. Model predictive control for wind power gradients

    DEFF Research Database (Denmark)

    Hovgaard, Tobias Gybel; Boyd, Stephen; Jørgensen, John Bagterp

    2015-01-01

    We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy generated while respecting limits on the time derivative (gradient) of power delivered to the grid. We...... wind data and modern wind forecasting methods. The simulation results using real wind data demonstrate the ability to reject the disturbances from fast changes in wind speed, ensuring certain power gradients, with an insignificant loss in energy production....... ranges. The system dynamics are quite non-linear, and the constraints and objectives are not convex functions of the control inputs, so the resulting optimal control problem is difficult to solve globally. In this paper, we show that by a novel change of variables, which focuses on power flows, we can...

  10. Alpha Power Predicts Persistence of Bistable Perception

    NARCIS (Netherlands)

    Piantoni, Giovanni; Romeijn, Nico; Gomez-Herrero, German; Van Der Werf, Ysbrand D; Van Someren, Eus J W

    2017-01-01

    Perception is strongly affected by the intrinsic state of the brain, which controls the propensity to either maintain a particular perceptual interpretation or switch to another. To understand the mechanisms underlying the spontaneous drive of the brain to explore alternative interpretations of

  11. Prediction of Wind Energy Resources (PoWER) Users Guide

    Science.gov (United States)

    2016-01-01

    ARL-TR-7573● JAN 2016 US Army Research Laboratory Prediction of Wind Energy Resources ( PoWER ) User’s Guide by David P Sauter...not return it to the originator. ARL-TR-7573 ● JAN 2016 US Army Research Laboratory Prediction of Wind Energy Resources ( PoWER ...2016 2. REPORT TYPE Final 3. DATES COVERED (From - To) 09/2015–11/2015 4. TITLE AND SUBTITLE Prediction of Wind Energy Resources ( PoWER ) User’s

  12. The Predictive Power of Subjective Probability Questions

    NARCIS (Netherlands)

    de Bresser, Jochem; van Soest, Arthur

    2017-01-01

    This paper evaluates the predictive validity of stated intentions for actual behaviour. In the context of the 2017 Dutch parliamentary election, we compare how well polls based on probabilistic and deterministic questions line up with subsequent votes. Our empirical strategy is built around a

  13. VT Predicted Mean Wind Power - 50 meter height

    Data.gov (United States)

    Vermont Center for Geographic Information — (Link to Metadata) Wind power predictions at 50m are generated by a numerical model that simulates weather conditions over a 15-year period, taking into account...

  14. Development of equipment reliability process using predictive technologies at Hamaoka Nuclear Power Station

    International Nuclear Information System (INIS)

    Taniguchi, Yuji; Sakuragi, Futoshi; Hamada, Seiichi

    2014-01-01

    Development of equipment reliability(ER) process, specifically for predictive maintenance (PdM) technologies integrated condition based maintenance (CBM) process, at Hamaoka Nuclear Power Station is introduced in this paper. Integration of predictive maintenance technologies such as vibration, oil analysis and thermo monitoring is more than important to establish strong maintenance strategies and to direct a specific technical development. In addition, a practical example of CBM is also presented to support the advantage of the idea. (author)

  15. Performance Prediction of Wind Power Turbine by CAD Analysis

    International Nuclear Information System (INIS)

    Kim, Jongho; Kim, Jongbong; Oh, Younglok

    2013-01-01

    The performance of a vertical-type wind power generator system was predicted by CAD analysis. In the analysis, the reaction torque was calculated for a given rotational speed of the blades. The blade torque of a wind power system was obtained for various rotational speeds, and the generation power was calculated using the obtained torque and the rotational speed. The optimum generator specification, therefore, could be decided using the relationship between the generated power and the rotational speeds. The effects of the number of blades and blade shapes on the generation power were also investigated. Finally, the analysis results were compared with the experimental results

  16. Prediction and design of first super-strong liquid-crystalline polymers

    International Nuclear Information System (INIS)

    Dowell, F.

    1989-01-01

    This paper presents the details of the theoretical prediction and design (atom by atom, bond by bond) of the molecule chemical structures of the first candidate super-strong liquid-crystalline polymers (SS LCPs). These LCPs are the first LCPs designed to have good compressive strengths, as well as to have tensile strengths and tensile moduli significantly larger than those of existing strong LCPs (such as Kevlar). The key feature of this new class of LCPs is that the exceptional strength is three dimensional on a microscopic, molecular level (thus, on a macroscopic level), in contrast to present LCPs (such as Kevlar) with their one-dimensional exceptional strength. These SS LCPs also have some solubility and processing advantages over existing strong LCPs. These SS LCPs are specially-designed combined LCPs such that the side chains of a molecule interdigitate with the side chains of other molecules. This paper also presents other essential general and specific features required for SS LCPs. Considerations in the design of SS LCPs include the spacing distance between side chains along the backbone, the need for rigid sections in the backbone and side chains, the degree of polymerization, the length of the side chains, the regularity of spacing of the side chains along the backbone, the interdigitation of side chains in submolecular strips, the packing of the side chains on one or two sides of the backbone, the symmetry of the side chains, the points of attachment of the side chains to the backbone, the flexibility and size of the chemical group connecting each side chain to the backbone, the effect of semiflexible sections in the backbone and side chains, and the choice of types of dipolar and/or hydrogen bonding forces in the backbones and side chains for easy alignment

  17. Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

    Energy Technology Data Exchange (ETDEWEB)

    Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng; Pota, Hemanshu; Gadh, Rajit

    2016-05-02

    This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.

  18. Evaluation of peak power prediction equations in male basketball players.

    Science.gov (United States)

    Duncan, Michael J; Lyons, Mark; Nevill, Alan M

    2008-07-01

    This study compared peak power estimated using 4 commonly used regression equations with actual peak power derived from force platform data in a group of adolescent basketball players. Twenty-five elite junior male basketball players (age, 16.5 +/- 0.5 years; mass, 74.2 +/- 11.8 kg; height, 181.8 +/- 8.1 cm) volunteered to participate in the study. Actual peak power was determined using a countermovement vertical jump on a force platform. Estimated peak power was determined using countermovement jump height and body mass. All 4 prediction equations were significantly related to actual peak power (all p jump prediction equations, 12% for the Canavan and Vescovi equation, and 6% for the Sayers countermovement jump equation. In all cases peak power was underestimated.

  19. Wind Power Plant Prediction by Using Neural Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Z.; Gao, W.; Wan, Y. H.; Muljadi, E.

    2012-08-01

    This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

  20. Introducing Model Predictive Control for Improving Power Plant Portfolio Performance

    DEFF Research Database (Denmark)

    Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon

    2008-01-01

    This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... implementation consisting of a distributed PI controller structure, both in terms of minimising the overall cost but also in terms of the ability to minimise deviation, which is the classical objective....

  1. Hybrid robust predictive optimization method of power system dispatch

    Science.gov (United States)

    Chandra, Ramu Sharat [Niskayuna, NY; Liu, Yan [Ballston Lake, NY; Bose, Sumit [Niskayuna, NY; de Bedout, Juan Manuel [West Glenville, NY

    2011-08-02

    A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.

  2. Skill forecasting from ensemble predictions of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

    2009-01-01

    Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

  3. Operational results from a physical power prediction model

    Energy Technology Data Exchange (ETDEWEB)

    Landberg, L. [Risoe National Lab., Meteorology and Wind Energy Dept., Roskilde (Denmark)

    1999-03-01

    This paper will describe a prediction system which predicts the expected power output of a number of wind farms. The system is automatic and operates on-line. The paper will quantify the accuracy of the predictions and will also give examples of the performance for specific storm events. An actual implementation of the system will be described and the robustness demonstrated. (au) 11 refs.

  4. LIBERAL MEASURES AND STRONG POWER. TO THE 190TH BIRTHDAY OF B.N. CHICHERIN

    Directory of Open Access Journals (Sweden)

    Aleksei Dmitrievich Nikotin

    2017-10-01

    Full Text Available Purpose: Identify the general and special in the views of Boris B. Chicherin, as the brightest representative of Russian liberalism at the turn of the XIX–XX centuries on the problem of Russia’s political system, the prospect of the development of civil and political freedoms. Research methodology: when working with the texts analyzed was used the comparative method, system method. The results of the study: The article examines the political and philosophical views of the greatest theoretician of Russian liberalism at the turn of the XIX–XX centuries B.N. Chicherin. The author, noting the breadth of scientific views of the Russian liberal, concentrated on his ideas about Russia’s preferred form of government and the need to reform Russia’s state and legal system. Particular attention is paid to the views of Boris N. Chicherin on the problem of the exercise of individual freedom of a person, his civil and political rights in the conditions of strong state power, building up the legal system of interaction between the state and civil society. The scope of the results: the results can be applied in scientific politological and social-philosophical studies, and in the course of teaching of social and Humanities disciplines.

  5. Glycerol Carbonate: A Novel Biosolvent with Strong Ionizing and Dissociating Powers

    Directory of Open Access Journals (Sweden)

    Guangnan Ou

    2012-01-01

    Full Text Available The activity of biocatalysts in nonaqueous solvents is related to the interaction of organic solvents with cells or enzymes. The behavior of proteins is strongly dependent on the protonation state of their ionizable groups, which ionization constants are greatly affected by the solvent. Due to the weak ionizing and dissociating powers of common organic solvents, the charge of the protein will change significantly when the protein is transferred from water to common organic solvents, resulting in protein denaturation. In this work, glycerol carbonate (GC was synthesized, which ionizing and dissociating abilities were very close to those of water. Transesterification activities of Candida antarctica lipase B (CALB in GC were comparable to those in water and remained constant during 4-week storage. Bacillus subtilis and Saccharomyecs cerevisiae were cultured in liquid media containing GC with test tubes. In the medium containing low GC concentration, Bacillus subtilis and Saccharomyecs cerevisiae grew well as in a medium containing no organic solvent, but, in the medium containing high GC concentration, the growth of Bacillus subtilis and Saccharomyecs cerevisiae was suppressed. The results suggested that GC is a potential biosolvent, which has great significance to biocatalysis in nonaqueous solvents.

  6. Regional Characterization of the Crust in Metropolitan Areas for Prediction of Strong Ground Motion

    Science.gov (United States)

    Hirata, N.; Sato, H.; Koketsu, K.; Umeda, Y.; Iwata, T.; Kasahara, K.

    2003-12-01

    Introduction: After the 1995 Kobe earthquake, the Japanese government increased its focus and funding of earthquake hazards evaluation, studies of man-made structures integrity, and emergency response planning in the major urban centers. A new agency, the Ministry of Education, Science, Sports and Culture (MEXT) has started a five-year program titled as Special Project for Earthquake Disaster Mitigation in Urban Areas (abbreviated to Dai-dai-toku in Japanese) since 2002. The project includes four programs: I. Regional characterization of the crust in metropolitan areas for prediction of strong ground motion. II. Significant improvement of seismic performance of structure. III. Advanced disaster management system. IV. Investigation of earthquake disaster mitigation research results. We will present the results from the first program conducted in 2002 and 2003. Regional Characterization of the Crust in Metropolitan Areas for Prediction of Strong Ground Motion: A long-term goal is to produce map of reliable estimations of strong ground motion. This requires accurate determination of ground motion response, which includes a source process, an effect of propagation path, and near surface response. The new five-year project was aimed to characterize the "source" and "propagation path" in the Kanto (Tokyo) region and Kinki (Osaka) region. The 1923 Kanto Earthquake is one of the important targets to be addressed in the project. The proximity of the Pacific and Philippine Sea subducting plates requires study of the relationship between earthquakes and regional tectonics. This project focuses on identification and geometry of: 1) Source faults, 2) Subducting plates and mega-thrust faults, 3) Crustal structure, 4) Seismogenic zone, 5) Sedimentary basins, 6) 3D velocity properties We have conducted a series of seismic reflection and refraction experiment in the Kanto region. In 2002 we have completed to deploy seismic profiling lines in the Boso peninsula (112 km) and the

  7. Local Geomagnetic Indices and the Prediction of Auroral Power

    Science.gov (United States)

    Newell, P. T.; Gjerloev, J. W.

    2014-12-01

    As the number of magnetometer stations and data processing power increases, just how auroral power relates to geomagnetic observations becomes a quantitatively more tractable question. This paper compares Polar UVI auroral power observations during 1997 with a variety of geomagnetic indices. Local time (LT) versions of the SuperMAG auroral electojet (SME) are introduced and examined, along with the corresponding upper and lower envelopes (SMU and SML). Also, the East-West component, BE, is investigated. We also consider whether using any of the local indices is actually better at predicting local auroral power than a single global index. Each index is separated into 24 LT indices based on a sliding 3-h MLT window. The ability to predict - or better reconstruct - auroral power varies greatly with LT, peaking at 1900 MLT, where about 75% of the variance (r2) can be predicted at 1-min cadence. The aurora is fairly predictable from 1700 MLT - 0400 MLT, roughly the region in which substorms occur. Auroral power is poorly predicted from auroral electrojet indices from 0500 MLT - 1500 MLT, with the minima at 1000-1300 MLT. In the region of high predictability, the local variable which works best is BE, in contrast to long-standing expectations. However using global SME is better than any local variable. Auroral power is best predicted by combining global SME with a local index: BE from 1500-0200 MLT, and either SMU or SML from 0300-1400 MLT. In the region of the diffuse aurora, it is better to use a 30 min average than the cotemporaneous 1-min SME value, while from 1500-0200 MLT the cotemporaneous 1-min SME works best, suggesting a more direct physical relationship with the auroral circuit. These results suggest a significant role for discrete auroral currents closing locally with Pedersen currents.

  8. Potentiality Prediction of Electric Power Replacement Based on Power Market Development Strategy

    Science.gov (United States)

    Miao, Bo; Yang, Shuo; Liu, Qiang; Lin, Jingyi; Zhao, Le; Liu, Chang; Li, Bin

    2017-05-01

    The application of electric power replacement plays an important role in promoting the development of energy conservation and emission reduction in our country. To exploit the potentiality of regional electric power replacement, the regional GDP (gross domestic product) and energy consumption are taken as potentiality evaluation indicators. The principal component factors are extracted with PCA (principal component analysis), and the integral potentiality analysis is made to the potentiality of electric power replacement in the national various regions; a region is taken as a research object, and the potentiality of electric power replacement is defined and quantified. The analytical model for the potentiality of multi-scenario electric power replacement is developed, and prediction is made to the energy consumption with the grey prediction model. The relevant theoretical research is utilized to realize prediction analysis on the potentiality amount of multi-scenario electric power replacement.

  9. Integrated Solid Oxide Fuel Cell Power System Characteristics Prediction

    Directory of Open Access Journals (Sweden)

    Marian GAICEANU

    2009-07-01

    Full Text Available The main objective of this paper is to deduce the specific characteristics of the CHP 100kWe Solid Oxide Fuel Cell (SOFC Power System from the steady state experimental data. From the experimental data, the authors have been developed and validated the steady state mathematical model. From the control room the steady state experimental data of the SOFC power conditioning are available and using the developed steady state mathematical model, the authors have been obtained the characteristic curves of the system performed by Siemens-Westinghouse Power Corporation. As a methodology the backward and forward power flow analysis has been employed. The backward power flow makes possible to obtain the SOFC power system operating point at different load levels, resulting as the load characteristic. By knowing the fuel cell output characteristic, the forward power flow analysis is used to predict the power system efficiency in different operating points, to choose the adequate control decision in order to obtain the high efficiency operation of the SOFC power system at different load levels. The CHP 100kWe power system is located at Gas Turbine Technologies Company (a Siemens Subsidiary, TurboCare brand in Turin, Italy. The work was carried out through the Energia da Ossidi Solidi (EOS Project. The SOFC stack delivers constant power permanently in order to supply the electric and thermal power both to the TurboCare Company and to the national grid.

  10. Prediction of lacking control power in power plants using statistical models

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Mataji, B.; Stoustrup, Jakob

    2007-01-01

    Prediction of the performance of plants like power plants is of interest, since the plant operator can use these predictions to optimize the plant production. In this paper the focus is addressed on a special case where a combination of high coal moisture content and a high load limits the possible...... errors; the second uses operating point depending statistics of prediction errors. Using these methods on the previous mentioned case, it can be concluded that the second method can be used to predict the power plant performance, while the first method has problems predicting the uncertain performance...... plant load, meaning that the requested plant load cannot be met. The available models are in this case uncertain. Instead statistical methods are used to predict upper and lower uncertainty bounds on the prediction. Two different methods are used. The first relies on statistics of recent prediction...

  11. Analysis of impact of “strong DC and weak AC” on receiving-end power system

    Science.gov (United States)

    Wang, Qiang; Li, Tianran; Yang, Pengcheng

    2018-02-01

    The rapid development of UHVDC transmission project has brought abundant power supply to the receiving-end power system area, but also many security and stability problems. This paper summarizes four elements that affect the strength of AC system, and then simulates the most basic two-terminal single-pole UHV transmission system by MATLAB/Simulink. It analyses the impact of receiving-end AC power system strength on real-time power, frequency and voltage. Finally, in view of operation risk of “strong DC and weak AC”, this paper puts forward three countermeasures.

  12. Performance and analysis of wireless power charging system from room temperature to HTS magnet via strong resonance coupling method

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Y. D.; Lee, S. Y.; Lee, T. W.; Kim, J. S. [Suwon Science College, Suwon (Korea, Republic of); Lee, C. Y. [Korea Railroad Institute, Uiwang (Korea, Republic of)

    2016-03-15

    The technology of supplying the electric power by wireless power transfer (WPT) is expected for the next generation power feeding system since it can supply the power to portable devices without any connectors through large air gap. As such a technology based on strongly coupled electromagnetic resonators is possible to deliver the large power and recharge them seamlessly; it has been considered as a noble option to wireless power charging system in the various power applications. Recently, various HTS wires have now been manufactured for demonstrations of transmission cables, motors, MAGLEV, and other electrical power components. However, since the HTS magnets have a lower index n value intrinsically, they are required to be charged from external power system through leads or internal power system. The portable area is limited as well as the cryogen system is bulkier. Thus, we proposed a novel design of wireless power charging system for superconducting HTS magnet (WPC4SM) based on resonance coupling method. As the novel system makes possible a wireless power charging using copper resonance coupled coils, it enables to portable charging conveniently in the superconducting applications. This paper presented the conceptual design and operating characteristics of WPC4SM using different shapes' copper resonance coil. The proposed system consists of four components; RF generator of 370 kHz, copper resonance coupling coils, impedance matching (IM) subsystem and HTS magnet including rectifier system.

  13. On the universality of power laws for tokamak plasma predictions

    Science.gov (United States)

    Garcia, J.; Cambon, D.; Contributors, JET

    2018-02-01

    Significant deviations from well established power laws for the thermal energy confinement time, obtained from extensive databases analysis as the IPB98(y,2), have been recently reported in dedicated power scans. In order to illuminate the adequacy, validity and universality of power laws as tools for predicting plasma performance, a simplified analysis has been carried out in the framework of a minimal modeling for heat transport which is, however, able to account for the interplay between turbulence and collinear effects with the input power known to play a role in experiments with significant deviations from such power laws. Whereas at low powers, the usual scaling laws are recovered with little influence of other plasma parameters, resulting in a robust power low exponent, at high power it is shown how the exponents obtained are extremely sensitive to the heating deposition, the q-profile or even the sampling or the number of points considered due to highly non-linear behavior of the heat transport. In particular circumstances, even a minimum of the thermal energy confinement time with the input power can be obtained, which means that the approach of the energy confinement time as a power law might be intrinsically invalid. Therefore plasma predictions with a power law approximation with a constant exponent obtained from a regression of a broad range of powers and other plasma parameters which can non-linearly affect and suppress heat transport, can lead to misleading results suggesting that this approach should be taken cautiously and its results continuously compared with modeling which can properly capture the underline physics, as gyrokinetic simulations.

  14. Predicting Harmonic Distortion of Multiple Converters in a Power System

    Directory of Open Access Journals (Sweden)

    P. M. Ivry

    2017-01-01

    Full Text Available Various uncertainties arise in the operation and management of power systems containing Renewable Energy Sources (RES that affect the systems power quality. These uncertainties may arise due to system parameter changes or design parameter choice. In this work, the impact of uncertainties on the prediction of harmonics in a power system containing multiple Voltage Source Converters (VSCs is investigated. The study focuses on the prediction of harmonic distortion level in multiple VSCs when some system or design parameters are only known within certain constraints. The Univariate Dimension Reduction (UDR method was utilized in this study as an efficient predictive tool for the level of harmonic distortion of the VSCs measured at the Point of Common Coupling (PCC to the grid. Two case studies were considered and the UDR technique was also experimentally validated. The obtained results were compared with that of the Monte Carlo Simulation (MCS results.

  15. Power Admission Control with Predictive Thermal Management in Smart Buildings

    DEFF Research Database (Denmark)

    Yao, Jianguo; Costanzo, Giuseppe Tommaso; Zhu, Guchuan

    2015-01-01

    This paper presents a control scheme for thermal management in smart buildings based on predictive power admission control. This approach combines model predictive control with budget-schedulability analysis in order to reduce peak power consumption as well as ensure thermal comfort. First......, the power budget with a given thermal comfort constraint is optimized through budget-schedulability analysis which amounts to solving a constrained linear programming problem. Second, the effective peak power demand is reduced by means of the optimal scheduling and cooperative operation of multiple thermal...... appliances. The performance of the proposed control scheme is assessed by simulation based on the thermal dynamics of a real eight-room office building located at Danish Technical University....

  16. When Power Shapes Interpersonal Behavior: Low Relationship Power Predicts Men’s Aggressive Responses to Low Situational Power

    Science.gov (United States)

    Overall, Nickola C.; Hammond, Matthew D.; McNulty, James K.; Finkel, Eli J.

    2016-01-01

    When does power in intimate relationships shape important interpersonal behaviors, such as psychological aggression? Five studies tested whether possessing low relationship power was associated with aggressive responses, but (1) only within power-relevant relationship interactions when situational power was low, and (2) only by men because masculinity (but not femininity) involves the possession and demonstration of power. In Studies 1 and 2, men lower in relationship power exhibited greater aggressive communication during couples’ observed conflict discussions, but only when they experienced low situational power because they were unable to influence their partner. In Study 3, men lower in relationship power reported greater daily aggressive responses toward their partner, but only on days when they experienced low situational power because they were either (a) unable to influence their partner or (b) dependent on their partner for support. In Study 4, men who possessed lower relationship power exhibited greater aggressive responses during couples’ support-relevant discussions, but only when they had low situational power because they needed high levels of support. Study 5 provided evidence for the theoretical mechanism underlying men’s aggressive responses to low relationship power. Men who possessed lower relationship power felt less manly on days they faced low situational power because their partner was unwilling to change to resolve relationship problems, which in turn predicted greater aggressive responses to their partner. These results demonstrate that fully understanding when and why power is associated with interpersonal behavior requires differentiating between relationship and situational power. PMID:27442766

  17. Improving Power Grid Resilience Through Predictive Outage Estimation

    OpenAIRE

    Eskandarpour, Rozhin; Khodaei, Amin; Arab, Ali

    2018-01-01

    In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the event, as well as predicted path and intensity of the upcoming extreme event. The outcome of the...

  18. Prediction of power-ramp defects in CANDU fuel

    International Nuclear Information System (INIS)

    Gillespie, P.; Wadsworth, S.; Daniels, T.

    2010-01-01

    Power ramps result in fuel pellet expansion and can lead to fuel sheath failures by fission product induced stress corrosion cracking (SCC). Historically, empirical models fit to experimental test data were used to predict the onset of power-ramp failures in CANDU fuel. In 1988, a power-ramped fuel defect event at PNGS-1 led to the refinement of these empirical models. This defect event has recently been re-analyzed and the empirical model updated. The empirical model is supported by a physically based model which can be used to extrapolate to fuel conditions (density, burnup) outside of the 1988 data set. (author)

  19. Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

    Directory of Open Access Journals (Sweden)

    Laura Cornejo-Bueno

    2017-11-01

    Full Text Available Wind Power Ramp Events (WPREs are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains. Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

  20. Combination of 24-Hour and 7-Day Relative Neurological Improvement Strongly Predicts 90-Day Functional Outcome of Endovascular Stroke Therapy.

    Science.gov (United States)

    Pu, Jie; Wang, Huaiming; Tu, Mingyi; Zi, Wenjie; Hao, Yonggang; Yang, Dong; Liu, Wenhua; Wan, Yue; Geng, Yu; Lin, Min; Jin, Ping; Xiong, Yunyun; Xu, Gelin; Yin, Qin; Liu, Xinfeng

    2018-01-03

    Early judgment of long-term prognosis is the key to making medical decisions in acute anterior circulation large-vessel occlusion stroke (LVOS) after endovascular treatment (EVT). We aimed to investigate the relationship between the combination of 24-hour and 7-day relative neurological improvement (RNI) and 90-day functional outcome. We selected the target population from a multicenter ischemic stroke registry. The National Institutes of Health Stroke Scale (NIHSS) scores at baseline, 24 hours, and 7 days were collected. RNI was calculated by the following equation: (baseline NIHSS - 24-hour/7-day NIHSS)/baseline NIHSS × 100%. A modified Rankin Scale score of 0-2 at 90 days was defined as a favorable outcome. Multivariable logistic regression analysis was used to evaluate the relationship between RNI and 90-day outcome. Receiver operator characteristic curve analysis was performed to identify the predictive power and cutoff point of RNI for functional outcome. A total of 568 patients were enrolled. Both 24-hour and 7-day RNI were independent predictors of 90-day outcome. The best cutoff points of 24-hour and 7-day RNI were 28% and 42%, respectively. Compared with those with 24-hour RNI of less than 28% and 7-day RNI of less than 42%, patients with 24-hour RNI of 28% or greater and 7-day RNI of 42% or greater had a 39.595-fold (95% confidence interval 22.388-70.026) increased probability of achieving 90-day favorable outcome. The combination of 24-hour and 7-day RNI very strongly predicts 90-day functional outcome in patients with acute anterior circulation LVOS who received EVT, and it can be used as an early accurate surrogate of long-term outcome. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  1. Peak power prediction in junior basketballers: comparing linear and allometric models.

    Science.gov (United States)

    Duncan, Michael J; Hankey, Joanne; Lyons, Mark; James, Rob S; Nevill, Alan M

    2013-03-01

    Equations, commonly used to predict peak power from jump height, have relied on linear additive models that are biologically unsound beyond the range of observations because of high negative intercept values. This study explored the utility of allometric multiplicative modeling to better predict peak power in adolescent basketball players. Seventy-seven elite junior basketball players (62 adolescent boys, 15 adolescent girls, age = 16.8 ± 0.8 years) performed 3 counter movement jumps (CMJs) on a force platform. Both linear and multiplicative models were then used to determine their efficacy. Four previously published linear equations were significantly associated with actual peak power (all p equations by Sayers (both p Allometric modeling was used to determine an alternative biologically sound equation which was more strongly associated with (r = 0.886, p 0.05), actual peak power and predicted 77.9% of the variance in actual peak power (adjusted R = 0.779, p equation was significantly associated (r = 0.871, p 0.05) and offered a more accurate estimation of peak power than previously validated linear additive models examined in this study. The allometric model determined from this study or the multiplicative model (body mass × CMJ height) provides biologically sound models to accurately estimate peak power in elite adolescent basketballers that are more accurate than equations based on linear additive models.

  2. Urban solar irradiance and power prediction from nearby stations

    Directory of Open Access Journals (Sweden)

    Zihao Chen

    2017-06-01

    Full Text Available With the proliferation of small-scale solar PV installations, global horizontal irradiance (GHI and power predictions are becoming critical elements in the integration of PV generation into the grid. This paper considers short-term prediction, from 5 minutes to a few hours, based on historical meteorological measurement data from weather and power monitoring stations located in the Canberra (Australia region. The specific objective of this study is to produce skilful forecasts for (a generic target station using a minimal amount of observations from nearby stations. Thus, although a large number of weather and power variables are collected and used for developing and testing the prediction algorithms, the ultimate aim is to rely on a few predictors, mainly meteorologically based. This will allow the identification of critical instruments which would need to be installed in order to provide satisfactory PV power predictions while limiting capital and operating costs of monitoring. Relative mean absolute error (rMAE is used here to indicate prediction performance. Three statistical methods are tested for two different seasons, a winter and a summer. The relative importance of predictors and stations is assessed. A conversion from GHI to global irradiance on tilted surfaces, by means of simple geometry arguments and notion of irradiance components at a nearby site, is also introduced and tested. Finally, the prediction accuracy is categorised according to different clear-sky indices. Results show that when the clear-sky index exceeds 0.9 (near-to-cloudless conditions, the prediction performance is distinctly better than at lower clear sky indices, by at least 0.05 and 0.2 in terms of rMAE in summer and winter, respectively.

  3. Methods for prediction of strong earthquake ground motion. Final technical report, October 1, 1976--September 30, 1977

    International Nuclear Information System (INIS)

    Trifunac, M.D.

    1977-09-01

    The purpose of this report is to summarize the results of the work on characterization of strong earthquake ground motion. The objective of this effort has been to initiate presentation of simple yet detailed methodology for characterization of strong earthquake ground motion for use in licensing and evaluation of operating Nuclear Power Plants. This report will emphasize the simplicity of the methodology by presenting only the end results in a format that may be useful for the development of the site specific criteria in seismic risk analysis, for work on the development of modern standards and regulatory guides, and for re-evaluation of the existing power plant sites

  4. Skill forecasting from different wind power ensemble prediction methods

    International Nuclear Information System (INIS)

    Pinson, Pierre; Nielsen, Henrik A; Madsen, Henrik; Kariniotakis, George

    2007-01-01

    This paper presents an investigation on alternative approaches to the providing of uncertainty estimates associated to point predictions of wind generation. Focus is given to skill forecasts in the form of prediction risk indices, aiming at giving a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the dispersion of ensemble members for a single prediction horizon, or over a set of successive look-ahead times. It is shown on the test case of a Danish offshore wind farm how prediction risk indices may be related to several levels of forecast uncertainty (and energy imbalances). Wind power ensemble predictions are derived from the transformation of ECMWF and NCEP ensembles of meteorological variables to power, as well as by a lagged average approach alternative. The ability of risk indices calculated from the various types of ensembles forecasts to resolve among situations with different levels of uncertainty is discussed

  5. Model output statistics applied to wind power prediction

    Energy Technology Data Exchange (ETDEWEB)

    Joensen, A.; Giebel, G.; Landberg, L. [Risoe National Lab., Roskilde (Denmark); Madsen, H.; Nielsen, H.A. [The Technical Univ. of Denmark, Dept. of Mathematical Modelling, Lyngby (Denmark)

    1999-03-01

    Being able to predict the output of a wind farm online for a day or two in advance has significant advantages for utilities, such as better possibility to schedule fossil fuelled power plants and a better position on electricity spot markets. In this paper prediction methods based on Numerical Weather Prediction (NWP) models are considered. The spatial resolution used in NWP models implies that these predictions are not valid locally at a specific wind farm. Furthermore, due to the non-stationary nature and complexity of the processes in the atmosphere, and occasional changes of NWP models, the deviation between the predicted and the measured wind will be time dependent. If observational data is available, and if the deviation between the predictions and the observations exhibits systematic behavior, this should be corrected for; if statistical methods are used, this approaches is usually referred to as MOS (Model Output Statistics). The influence of atmospheric turbulence intensity, topography, prediction horizon length and auto-correlation of wind speed and power is considered, and to take the time-variations into account, adaptive estimation methods are applied. Three estimation techniques are considered and compared, Extended Kalman Filtering, recursive least squares and a new modified recursive least squares algorithm. (au) EU-JOULE-3. 11 refs.

  6. Sludge pipe flow pressure drop prediction using composite power ...

    African Journals Online (AJOL)

    ... in 3 test pipe diameters, was established and used to rheologically characterise the sludges as Bingham plastic fluids. Five published definitions of the non-Newtonian Reynolds number were used to create composite power law correlations for the f-Re relationship covering all flow regimes. Pressure gradient predictions ...

  7. Electrical predictive maintenance at Trillo I Nuclear Power Plant

    International Nuclear Information System (INIS)

    Vicente, L. R.; Fernandez de la Mata, R.; Cano Gonzalez, J. C.

    1998-01-01

    An electrical predictive maintenance plan is currently being put into effect at Trillo I Nuclear Power Plant which is initially being applied to three types of equipment: motors, transformers and motor-driven valves. This paper describes the different phases considered in the implementation of the Predictive Maintenance Plan: study of existing techniques for such equipment (tangoδ, spectral analysis of stator current, chromatographic analysis of gases, spectral analysis of the axial stray magnetic flux, etc), study of the special characteristics of the electrical equipment at Trillo NPP, analysis of applicable techniques (characteristic parameters, alert-alarm values, experience with such techniques, etc), analysis of machine history records, study of the optimum preventive-predictive case, study of applicable frequencies and definition of the computerised predictive maintenance management tool. With the exception of the computerised predictive maintenance management applications which are presently being implemented, all the activities described above have been carried out on the three types of equipment mentioned. (Author)

  8. Predictive Maintenance: One key to improved power plant availability

    International Nuclear Information System (INIS)

    Mobley; Allen, J.W.

    1986-01-01

    Recent developments in microprocessor technology has provided the ability to routinely monitor the actual mechanical condition of all rotating and reciprocating machinery and process variables (i.e. pressure, temperature, flow, etc.) of other process equipment within an operating electric power generating plant. This direct correlation between frequency domain vibration and actual mechanical condition of machinery and trending process variables of non-rotating equipment can provide the ''key'' to improving the availability and reliability, thermal efficiency and provide the baseline information necessary for developing a realistic plan for extending the useful life of power plants. The premise of utilizing microprocessor-based Predictive Maintenance to improve power plant operation has been proven by a number of utilities. This paper provides a comprehensive discussion of the TEC approach to Predictive Maintenance and examples of successful programs

  9. Effect of accuracy of wind power prediction on power system operator

    Science.gov (United States)

    Schlueter, R. A.; Sigari, G.; Costi, T.

    1985-01-01

    This research project proposed a modified unit commitment that schedules connection and disconnection of generating units in response to load. A modified generation control is also proposed that controls steam units under automatic generation control, fast responding diesels, gas turbines and hydro units under a feedforward control, and wind turbine array output under a closed loop array control. This modified generation control and unit commitment require prediction of trend wind power variation one hour ahead and the prediction of error in this trend wind power prediction one half hour ahead. An improved meter for predicting trend wind speed variation is developed. Methods for accurately simulating the wind array power from a limited number of wind speed prediction records was developed. Finally, two methods for predicting the error in the trend wind power prediction were developed. This research provides a foundation for testing and evaluating the modified unit commitment and generation control that was developed to maintain operating reliability at a greatly reduced overall production cost for utilities with wind generation capacity.

  10. Effect of accuracy of wind power prediction on power system operator

    Science.gov (United States)

    Schlueter, R. A.; Sigari, G.; Costi, T.

    1985-06-01

    This research project proposed a modified unit commitment that schedules connection and disconnection of generating units in response to load. A modified generation control is also proposed that controls steam units under automatic generation control, fast responding diesels, gas turbines and hydro units under a feedforward control, and wind turbine array output under a closed loop array control. This modified generation control and unit commitment require prediction of trend wind power variation one hour ahead and the prediction of error in this trend wind power prediction one half hour ahead. An improved meter for predicting trend wind speed variation is developed. Methods for accurately simulating the wind array power from a limited number of wind speed prediction records was developed. Finally, two methods for predicting the error in the trend wind power prediction were developed. This research provides a foundation for testing and evaluating the modified unit commitment and generation control that was developed to maintain operating reliability at a greatly reduced overall production cost for utilities with wind generation capacity.

  11. An overview of the reliability prediction related aspects of high power IGBTs in wind power applications

    DEFF Research Database (Denmark)

    Busca, Christian; Teodorescu, Remus; Blaabjerg, Frede

    2011-01-01

    Reliability is becoming more and more important as the size and number of installed Wind Turbines (WTs) increases. Very high reliability is especially important for offshore WTs because the maintenance and repair of such WTs in case of failures can be very expensive. WT manufacturers need...... to consider the reliability aspect when they design new power converters. By designing the power converter considering the reliability aspect the manufacturer can guarantee that the end product will ensure high availability. This paper represents an overview of the various aspects of reliability prediction...... of high power Insulated Gate Bipolar Transistors (IGBTs) in the context of wind power applications. At first the latest developments and future predictions about wind energy are briefly discussed. Next the dominant failure mechanisms of high power IGBTs are described and the most commonly used lifetime...

  12. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  13. Strong doping of the n-optical confinement layer for increasing output power of high- power pulsed laser diodes in the eye safe wavelength range

    Science.gov (United States)

    Ryvkin, Boris S.; Avrutin, Eugene A.; Kostamovaara, Juha T.

    2017-12-01

    An analytical model for internal optical losses at high power in a 1.5 μm laser diode with strong n-doping in the n-side of the optical confinement layer is created. The model includes intervalence band absorption by holes supplied by both current flow and two-photon absorption (TPA), as well as the direct TPA effect. The resulting losses are compared with those in an identical structure with a weakly doped waveguide, and shown to be substantially lower, resulting in a significant improvement in the output power and efficiency in the structure with a strongly doped waveguide.

  14. Using meteorological forecasts in on-line predictions of wind power

    DEFF Research Database (Denmark)

    Nielsen, Torben Skov; Nielsen, Henrik Aalborg; Madsen, Henrik

    1999-01-01

    This report describes a model investigation into wind power prediction model as well as a tool for predicting the power production from wind turbines in an area - the Wind Power Prediction Tool (WPPT). The predictions are based on on-line measurements of power production for a selected set...

  15. Power system dynamic state estimation using prediction based evolutionary technique

    International Nuclear Information System (INIS)

    Basetti, Vedik; Chandel, Ashwani K.; Chandel, Rajeevan

    2016-01-01

    In this paper, a new robust LWS (least winsorized square) estimator is proposed for dynamic state estimation of a power system. One of the main advantages of this estimator is that it has an inbuilt bad data rejection property and is less sensitive to bad data measurements. In the proposed approach, Brown's double exponential smoothing technique has been utilised for its reliable performance at the prediction step. The state estimation problem is solved as an optimisation problem using a new jDE-self adaptive differential evolution with prediction based population re-initialisation technique at the filtering step. This new stochastic search technique has been embedded with different state scenarios using the predicted state. The effectiveness of the proposed LWS technique is validated under different conditions, namely normal operation, bad data, sudden load change, and loss of transmission line conditions on three different IEEE test bus systems. The performance of the proposed approach is compared with the conventional extended Kalman filter. On the basis of various performance indices, the results thus obtained show that the proposed technique increases the accuracy and robustness of power system dynamic state estimation performance. - Highlights: • To estimate the states of the power system under dynamic environment. • The performance of the EKF method is degraded during anomaly conditions. • The proposed method remains robust towards anomalies. • The proposed method provides precise state estimates even in the presence of anomalies. • The results show that prediction accuracy is enhanced by using the proposed model.

  16. Discussion of fostering strong nuclear safety culture in nuclear power plants in China

    International Nuclear Information System (INIS)

    Jiang Fuming

    2011-01-01

    This paper described the most recent development of nuclear safety culture in the world nuclear industry. Focus areas are recommended to foster a strong nuclear safety culture (SNSC) in Chinese nuclear industry with the view of our current development, aiming to accelerate the formation of SNSC. (author)

  17. Predictive power of renormalisation group flows a comparison

    CERN Document Server

    Litim, Daniel F; Litim, Daniel F.; Pawlowski, Jan M.

    2001-01-01

    We study a proper-time renormalisation group, which is based on an operator cut-off regularisation of the one-loop effective action. The predictive power of this approach is constrained because the flow is not an exact one. We compare it to the Exact Renormalisation Group, which is based on a momentum regulator in the Wilsonian sense. In contrast to the former, the latter provides an exact flow. To leading order in a derivative expansion, an explicit map from the exact to the proper-time renormalisation group is established. The opposite map does not exist in general. We discuss various implications of these findings, in particular in view of the predictive power of the proper-time renormalisation group. As an application, we compute critical exponents for O(N)-symmetric scalar theories at the Wilson-Fisher fixed point in 3d from both formalisms.

  18. Departures from predicted type II behavior in dirty strong-coupling superconductors

    International Nuclear Information System (INIS)

    Park, J.C.; Neighbor, J.E.; Shiffman, C.A.

    1976-01-01

    Calorimetric measurements of the Ginsburg-Landau parameters for Pb-Sn and Pb-Bi alloys show good agreement with the calculations of Rainer and Bergmann for kappa 1 (t)/kappa 1 (1). However, the calculations of Rainer and Usadel for kappa 2 (t)/kappa 2 (1) substantially underestimate the enhancements due to strong-coupling. (Auth.)

  19. Predicting Output Power for Nearshore Wave Energy Harvesting

    Directory of Open Access Journals (Sweden)

    Henock Mamo Deberneh

    2018-04-01

    Full Text Available Energy harvested from a Wave Energy Converter (WEC varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In this paper, we present a novel approach to predicting the output power of a nearshore WEC by characterizing ocean waves using floating buoys. We monitored the movement of the buoys using an Arduino-based data collection module, including a gyro-accelerometer sensor and a wireless transceiver. The collected data were utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF and ANN. The results of the experiments showed that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, we found that the predictors work better when the regressors are combined with a classifier. The accuracy of the proposed prediction model suggests that it could be extremely useful in both locating optimal placement for wave energy harvesting plants and designing the shape of the buoys used by them.

  20. Error analysis of short term wind power prediction models

    International Nuclear Information System (INIS)

    De Giorgi, Maria Grazia; Ficarella, Antonio; Tarantino, Marco

    2011-01-01

    The integration of wind farms in power networks has become an important problem. This is because the electricity produced cannot be preserved because of the high cost of storage and electricity production must follow market demand. Short-long-range wind forecasting over different lengths/periods of time is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based upon the valid assumption that all the causative factors are implicitly accounted for in the sequence of occurrence of the process itself. Hence time series modelling is equivalent to physical modelling. Auto Regressive Moving Average (ARMA) models, which perform a linear mapping between inputs and outputs, and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which perform a non-linear mapping, provide a robust approach to wind power prediction. In this work, these models are developed in order to forecast power production of a wind farm with three wind turbines, using real load data and comparing different time prediction periods. This comparative analysis takes in the first time, various forecasting methods, time horizons and a deep performance analysis focused upon the normalised mean error and the statistical distribution hereof in order to evaluate error distribution within a narrower curve and therefore forecasting methods whereby it is more improbable to make errors in prediction. (author)

  1. Whose intentions predict? Power over condom use within heterosexual dyads.

    Science.gov (United States)

    VanderDrift, Laura E; Agnew, Christopher R; Harvey, S Marie; Warren, Jocelyn T

    2013-10-01

    According to major theories of behavioral prediction, the most proximal psychological predictor of an individual's behavior is that individual's intention. With respect to interdependent behaviors such as condom use, however, relationship dynamics influence individuals' power to make decisions and to act. The current study examines how relationship dynamics impact 3 condom use relevant outcomes: (a) the individual forming his or her own intention to use condoms, (b) the couple forming their joint intention to use condoms, and (c) actual condom use behavior. We conducted a 2-wave longitudinal study of young heterosexual adult couples at high risk for HIV infection involving the collection of both individual- and couple-derived data. Results demonstrate the importance of both person (e.g., biological sex and dispositional dominance) and relational (e.g., relational power and amount of interest in the relationship, operationalized as commitment and perceived alternatives to the relationship) factors in predicting condom use intentions and behavior. Individuals who are lower in dispositional dominance are likely to incorporate their partner's intentions into their own individual intentions; the intentions of individuals who have less interest in the relationship are more highly predictive of the couple's joint intention; and the intentions of men and individuals higher in relationship power are more likely to exert a direct influence on condom use. These findings have implications for improving the health of high-risk individuals, including suggesting situations in which individuals are highly influenced by their partners' intentions. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

  2. Prediction of Chiller Power Consumption: An Entropy Generation Approach

    KAUST Repository

    Saththasivam, Jayaprakash

    2016-06-21

    Irreversibilities in each component of vapor compression chillers contribute to additional power consumption in chillers. In this study, chiller power consumption was predicted by computing the Carnot reversible work and entropy generated in every component of the chiller. Thermodynamic properties namely enthalpy and entropy of the entire refrigerant cycle were obtained by measuring the pressure and temperature at the inlet and outlet of each primary component of a 15kW R22 water cooled scroll chiller. Entropy generation of each component was then calculated using the First and Second Laws of Thermodynamics. Good correlation was found between the measured and computed chiller power consumption. This irreversibility analysis can be also effectively used as a performance monitoring tool in vapor compression chillers as higher entropy generation is anticipated during faulty operations.

  3. Thermal Storage Power Balancing with Model Predictive Control

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Poulsen, Niels Kjølstad; Madsen, Henrik

    2013-01-01

    . The total power consumption of all loads is controlled indirectly through a real-time price. The MPC incorporates forecasts of the power production and disturbances that influence the loads, e.g. time-varying weather forecasts, in order to react ahead of time. A simulation scenario demonstrates......The method described in this paper balances power production and consumption with a large number of thermal loads. Linear controllers are used for the loads to track a temperature set point, while Model Predictive Control (MPC) and model estimation of the load behavior are used for coordination...... that the method allows for the integration of flexible thermal loads in a smart energy system in which consumption follows the changing production....

  4. Serum MHPG Strongly Predicts Conversion to Alzheimer's Disease in Behaviorally Characterized Subjects with Down Syndrome

    NARCIS (Netherlands)

    Dekker, Alain D.; Coppus, Antonia M. W.; Vermeiren, Yannick; Aerts, Tony; van Duijn, Cornelia M.; Kremer, Berry P.; Naude, Pieter J. W.; Van Dam, Debby; De Deyn, Peter P.

    2015-01-01

    Background: Down syndrome (DS) is the most prevalent genetic cause of intellectual disability. Early-onset Alzheimer's disease (AD) frequently develops in DS and is characterized by progressive memory loss and behavioral and psychological signs and symptoms of dementia (BPSD). Predicting and

  5. Strong homing does not predict high site fidelity in juvenile reef fishes

    Science.gov (United States)

    Streit, Robert P.; Bellwood, David R.

    2018-03-01

    After being displaced, juvenile reef fishes are able to return home over large distances. This strong homing behaviour is extraordinary and may allow insights into the longer-term spatial ecology of fish communities. For example, it appears intuitive that strong homing behaviour should be indicative of long-term site fidelity. However, this connection has rarely been tested. We quantified the site fidelity of juvenile fishes of four species after returning home following displacement. Two species, parrotfishes and Pomacentrus moluccensis, showed significantly reduced site fidelity after returning home. On average, they disappeared from their home sites almost 3 d earlier than expected. Mortality or competitive exclusion does not seem to be the main reasons for their disappearance. Rather, we suggest an increased propensity to relocate after encountering alternative reef locations while homing. It appears that some juvenile fishes may have a higher innate spatial flexibility than their strict homing drive suggests.

  6. Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

    Science.gov (United States)

    Aissa, Oualid; Moulahoum, Samir; Colak, Ilhami; Babes, Badreddine; Kabache, Nadir

    2017-10-12

    This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.

  7. Strongly increasing solutions of cyclic systems of second order differential equations with power-type nonlinearities

    Directory of Open Access Journals (Sweden)

    Jaroslav Jaroš

    2015-01-01

    Full Text Available We consider \\(n\\-dimensional cyclic systems of second order differential equations \\[(p_i(t|x_{i}'|^{\\alpha_i -1}x_{i}'' = q_{i}(t|x_{i+1}|^{\\beta_i-1}x_{i+1},\\] \\[\\quad i = 1,\\ldots,n, \\quad (x_{n+1} = x_1 \\tag{\\(\\ast\\}\\] under the assumption that the positive constants \\(\\alpha_i\\ and \\(\\beta_i\\ satisfy \\(\\alpha_1{\\ldots}\\alpha_n \\gt \\beta_1{\\ldots}\\beta_n\\ and \\(p_i(t\\ and \\(q_i(t\\ are regularly varying functions, and analyze positive strongly increasing solutions of system (\\(\\ast\\ in the framework of regular variation. We show that the situation for the existence of regularly varying solutions of positive indices for (\\(\\ast\\ can be characterized completely, and moreover that the asymptotic behavior of such solutions is governed by the unique formula describing their order of growth precisely. We give examples demonstrating that the main results for (\\(\\ast\\ can be applied to some classes of partial differential equations with radial symmetry to acquire accurate information about the existence and the asymptotic behavior of their radial positive strongly increasing solutions.

  8. Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures.

    Science.gov (United States)

    Lai, Ying-Cheng; Harrison, Mary Ann F; Frei, Mark G; Osorio, Ivan

    2004-09-01

    Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy. Copyright 2004 American Institute of Physics

  9. Predicting Power Outages Using Multi-Model Ensemble Forecasts

    Science.gov (United States)

    Cerrai, D.; Anagnostou, E. N.; Yang, J.; Astitha, M.

    2017-12-01

    Power outages affect every year millions of people in the United States, affecting the economy and conditioning the everyday life. An Outage Prediction Model (OPM) has been developed at the University of Connecticut for helping utilities to quickly restore outages and to limit their adverse consequences on the population. The OPM, operational since 2015, combines several non-parametric machine learning (ML) models that use historical weather storm simulations and high-resolution weather forecasts, satellite remote sensing data, and infrastructure and land cover data to predict the number and spatial distribution of power outages. A new methodology, developed for improving the outage model performances by combining weather- and soil-related variables using three different weather models (WRF 3.7, WRF 3.8 and RAMS/ICLAMS), will be presented in this study. First, we will present a performance evaluation of each model variable, by comparing historical weather analyses with station data or reanalysis over the entire storm data set. Hence, each variable of the new outage model version is extracted from the best performing weather model for that variable, and sensitivity tests are performed for investigating the most efficient variable combination for outage prediction purposes. Despite that the final variables combination is extracted from different weather models, this ensemble based on multi-weather forcing and multi-statistical model power outage prediction outperforms the currently operational OPM version that is based on a single weather forcing variable (WRF 3.7), because each model component is the closest to the actual atmospheric state.

  10. Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

    Directory of Open Access Journals (Sweden)

    Anders Hedegaard Hansen

    2018-03-01

    Full Text Available Wave power extraction algorithms for wave energy converters are normally designed without taking system losses into account leading to suboptimal power extraction. In the current work, a model predictive power extraction algorithm is designed for a discretized power take of system. It is shown how the quantized nature of a discrete fluid power system may be included in a new model predictive control algorithm leading to a significant increase in the harvested power. A detailed investigation of the influence of the prediction horizon and the time step is reported. Furthermore, it is shown how the inclusion of a loss model may increase the energy output. Based on the presented results it is concluded that power extraction algorithms based on model predictive control principles are both feasible and favorable for use in a discrete fluid power power take-off system for point absorber wave energy converters.

  11. In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes.

    Science.gov (United States)

    Schmidt, Julien; Guillaume, Philippe; Dojcinovic, Danijel; Karbach, Julia; Coukos, George; Luescher, Immanuel

    2017-07-14

    Tumor exomes provide comprehensive information on mutated, overexpressed genes and aberrant splicing, which can be exploited for personalized cancer immunotherapy. Of particular interest are mutated tumor antigen T-cell epitopes, because neoepitope-specific T cells often are tumoricidal. However, identifying tumor-specific T-cell epitopes is a major challenge. A widely used strategy relies on initial prediction of human leukocyte antigen-binding peptides by in silico algorithms, but the predictive power of this approach is unclear. Here, we used the human tumor antigen NY-ESO-1 (ESO) and the human leukocyte antigen variant HLA-A*0201 (A2) as a model and predicted in silico the 41 highest-affinity, A2-binding 8-11-mer peptides and assessed their binding, kinetic complex stability, and immunogenicity in A2-transgenic mice and on peripheral blood mononuclear cells from ESO-vaccinated melanoma patients. We found that 19 of the peptides strongly bound to A2, 10 of which formed stable A2-peptide complexes and induced CD8 + T cells in A2-transgenic mice. However, only 5 of the peptides induced cognate T cells in humans; these peptides exhibited strong binding and complex stability and contained multiple large hydrophobic and aromatic amino acids. These results were not predicted by in silico algorithms and provide new clues to improving T-cell epitope identification. In conclusion, our findings indicate that only a small fraction of in silico -predicted A2-binding ESO peptides are immunogenic in humans, namely those that have high peptide-binding strength and complex stability. This observation highlights the need for improving in silico predictions of peptide immunogenicity. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  12. Diabetic Retinopathy Is Strongly Predictive of Cardiovascular Autonomic Neuropathy in Type 2 Diabetes.

    Science.gov (United States)

    Huang, Chih-Cheng; Lee, Jong-Jer; Lin, Tsu-Kung; Tsai, Nai-Wen; Huang, Chi-Ren; Chen, Shu-Fang; Lu, Cheng-Hsien; Liu, Rue-Tsuan

    2016-01-01

    A well-established, comprehensive, and simple test battery was used here to re-evaluate risk factors for cardiovascular autonomic neuropathy (CAN) in type 2 diabetes. One hundred and seventy-four patients with type 2 diabetes were evaluated through the methods of deep breathing and Valsalva maneuver for correlation with factors that might influence the presence and severity of CAN. The Composite Autonomic Scoring Scale (CASS) was used to grade the severity of autonomic impairment, and CAN was defined as a CASS score ≥2. Results showed that nephropathy, duration of diabetes, blood pressure, uric acid, and the presence of retinopathy and metabolic syndrome significantly correlated with the CASS score. Age may not be a risk factor for diabetic CAN. However, the effects of diabetes on CAN are more prominent in younger patients than in older ones. Diabetic retinopathy is the most significant risk factor predictive of the presence of CAN in patients with type 2 diabetes.

  13. A mathematical look at a physical power prediction model

    DEFF Research Database (Denmark)

    Landberg, L.

    1998-01-01

    This article takes a mathematical look at a physical model used to predict the power produced from wind farms. The reason is to see whether simple mathematical expressions can replace the original equations and to give guidelines as to where simplifications can be made and where they cannot....... The article shows that there is a linear dependence between the geostrophic wind and the local wind at the surface, but also that great care must be taken in the selection of the simple mathematical models, since physical dependences play a very important role, e.g. through the dependence of the turning...

  14. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time......-varying regression model. In a second step, time-series models, i.e., ARMA and Holt–Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pool's Elspot, during a two...

  15. Predicting the biological variability of environmental rhythms: weak or strong anticipation for sensorimotor synchronization?

    Science.gov (United States)

    Torre, Kjerstin; Varlet, Manuel; Marmelat, Vivien

    2013-12-01

    The internal processes involved in synchronizing our movements with environmental stimuli have traditionally been addressed using regular metronomic sequences. Regarding real-life environments, however, biological rhythms are known to have intrinsic variability, ubiquitously characterized as fractal long-range correlations. In our research we thus investigate to what extent the synchronization processes drawn from regular metronome paradigms can be generalized to other (biologically) variable rhythms. Participants performed synchronized finger tapping under five conditions of long-range and/or short-range correlated, randomly variable, and regular auditory sequences. Combining experimental data analysis and numerical simulation, we found that synchronizing with biologically variable rhythms involves the same internal processes as with other variable rhythms (whether totally random or comprising lawful regularities), but different from those involved with a regular metronome. This challenges both the generalizability of conclusions drawn from regular-metronome paradigms, and recent research assuming that biologically variable rhythms may trigger specific strong anticipatory processes to achieve synchronization. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Acute post cessation smoking. A strong predictive factor for metabolic syndrome among adult Saudis

    International Nuclear Information System (INIS)

    AlDaghri, Nasser M.

    2009-01-01

    To determine the influence of tobacco exposure in the development of metabolic syndrome (MS) in the adult Saudi population. Six hundred and sixty-four adults (305 males and 359 females) aged 25-70 years were included in this cross-sectional study conducted at the King Abdul Aziz University Hospital, between June 2006 and May 2007. We classified the participants into non-smokers, smokers, and ex-smokers (defined as complete cessation for 1-2 years). All subjects were screened for the presence of MS using the modified American Heart Association/National Heart, Lung and Blood Institute (AHA/NHLBI), International Diabetes Federation (IDF) and World Health Organization (WHO) definitions. Metabolic syndrome was highest among ex-smokers regardless of definition used. Relative risk for ex-smokers (95% CI: 2.23, 1.06-4.73) was more than twice in harboring MS as compared to non-smokers (95% CI: 2.78, 1.57-4.92) (p=0.009). Acute post-cessation smoking is a strong predictor for MS among male and female Arabs. Smoking cessation programs should include a disciplined lifestyle and dietary intervention to counteract the MS-augmenting side-effect of smoking cessation. (author)

  17. Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

  18. Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

    DEFF Research Database (Denmark)

    Hansen, Anders Hedegaard; Asmussen, Magnus Færing; Bech, Michael Møller

    2018-01-01

    Wave power extraction algorithms for wave energy converters are normally designed without taking system losses into account leading to suboptimal power extraction. In the current work, a model predictive power extraction algorithm is designed for a discretized power take of system. It is shown ho...

  19. On the thermoelectric power in degenerate narrow gap semiconductors in the presence of a strong magnetic field

    International Nuclear Information System (INIS)

    Ghatak, K.P.; De, B.

    1991-01-01

    In this paper the authors have studied the thermoelectric power under strong magnetic field in degenerate semiconductors on the basis of fourth order in affective mass theory and taking into account the interactions of the conduction electrons, heavy-holes, light-holes and split-off holes respectively. The results obtained are then compared to those derived on the basis of the well-known three-band Kane model. It is found, taking n-Hg 1-x Cd x Te as an example, that the magneto-thermo power increases with decreasing electron concentration and increasing magnetic field respectively for both the models in an oscillatory way. The oscillations are due to SdH effects and the theoretical analysis in accordance with fourth order in effective mass theory i in agreement with the experimental observation as reported elsewhere. In addition, the corresponding results for parabolic energy bands have also been obtained as special cases of our generalized formulations

  20. Optimization of an overmoded smooth-wall circular waveguide section for carrying strong MM-wave power in ECRH experiments

    International Nuclear Information System (INIS)

    Bille, F.; Mania, L.; Viciguerra, G.; Granucci, G.; Simonetto, A.

    1993-01-01

    In this paper the authors explore the possibility of using sections of overmoded smooth-wall circular waveguide instead of an overall corrugated waveguide transmission line to carry strong mm-wave power in ECRH experiments. To this end they carry out an exact analysis of the transitions from corrugated to smooth-wall waveguide and optimize the length and size of the smooth-wall waveguide to achieve minimum losses. The whole structure is described by a generalized scattering matrix whose elements are obtained using the exact quasi-TE and quasi-TM uncoupled modes in the lossy smooth-wall waveguide

  1. Epileptic seizure prediction based on a bivariate spectral power methodology.

    Science.gov (United States)

    Bandarabadi, Mojtaba; Teixeira, Cesar A; Direito, Bruno; Dourado, Antonio

    2012-01-01

    The spectral power of 5 frequently considered frequency bands (Alpha, Beta, Gamma, Theta and Delta) for 6 EEG channels is computed and then all the possible pairwise combinations among the 30 features set, are used to create a 435 dimensional feature space. Two new feature selection methods are introduced to choose the best candidate features among those and to reduce the dimensionality of this feature space. The selected features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and non-preictal classes. The outputs of the SVM are regularized using a method that accounts for the classification dynamics of the preictal class, also known as "Firing Power" method. The results obtained using our feature selection approaches are compared with the ones obtained using minimum Redundancy Maximum Relevance (mRMR) feature selection method. The results in a group of 12 patients of the EPILEPSIAE database, containing 46 seizures and 787 hours multichannel recording for out-of-sample data, indicate the efficiency of the bivariate approach as well as the two new feature selection methods. The best results presented sensitivity of 76.09% (35 of 46 seizures predicted) and a false prediction rate of 0.15(-1).

  2. Method for Prediction of the Power Output from Photovoltaic Power Plant under Actual Operating Conditions

    Science.gov (United States)

    Obukhov, S. G.; Plotnikov, I. A.; Surzhikova, O. A.; Savkin, K. D.

    2017-04-01

    Solar photovoltaic technology is one of the most rapidly growing renewable sources of electricity that has practical application in various fields of human activity due to its high availability, huge potential and environmental compatibility. The original simulation model of the photovoltaic power plant has been developed to simulate and investigate the plant operating modes under actual operating conditions. The proposed model considers the impact of the external climatic factors on the solar panel energy characteristics that improves accuracy in the power output prediction. The data obtained through the photovoltaic power plant operation simulation enable a well-reasoned choice of the required capacity for storage devices and determination of the rational algorithms to control the energy complex.

  3. Characterizing and predicting the robustness of power-law networks

    International Nuclear Information System (INIS)

    LaRocca, Sarah; Guikema, Seth D.

    2015-01-01

    Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2000 randomly-generated power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing networks such as the Internet and cellular metabolic pathways, and efficiently degrading networks such as terrorist cells. - Highlights: • Examine relationship between network topology and robustness to failures. • Relationship is statistically significant for scale-free networks. • Use statistical models to estimate robustness to failures for real-world networks

  4. Procedure to predict the storey where plastic drift dominates in two-storey building under strong ground motion

    DEFF Research Database (Denmark)

    Hibino, Y.; Ichinose, T.; Costa, J.L.D.

    2009-01-01

    A procedure is presented to predict the storey where plastic drift dominates in two-storey buildings under strong ground motion. The procedure utilizes the yield strength and the mass of each storey as well as the peak ground acceleration. The procedure is based on two different assumptions: (1......) the seismic force distribution is of inverted triangular form and (2) the rigid-plastic model represents the system. The first and the second assumptions, respectively, lead to lower and upper estimates of the base shear coefficient under which the drift of the first storey exceeds that of the second storey...

  5. Model predictive control for Z-source power converter

    DEFF Research Database (Denmark)

    Mo, W.; Loh, P.C.; Blaabjerg, Frede

    2011-01-01

    This paper presents Model Predictive Control (MPC) of impedance-source (commonly known as Z-source) power converter. Output voltage control and current control for Z-source inverter are analyzed and simulated. With MPC's ability of multi- system variables regulation, load current and voltage...... regulations, impedance network inductor current, capacitor voltage as well as switching frequency fixation, transient reservation and null state penalization are all regulated as subjecting to constraints of this control method. The quality of output waveform, stability of impedance-network, level constraint...... of variable switching frequency as well as robustness of transient response can be obtained at the same time with a formulated Z-source network model. Operating steady state and transient state simulation of MPC are going to be presented, which shows good reference tracking ability of this control method....

  6. Observed and predicted cooling tower plume rise at the John E. Amos Power Plant, West Virginia

    International Nuclear Information System (INIS)

    Hanna, S.R.

    1977-01-01

    There is much current interest in cooling tower plume rise because of its importance in determining the environmental impact of cooling towers at planned power plants and industrial facilities. Some of the possible environmental problems related to heat and water emissions from cooling towers are drift deposition, ground level fog, cloud formation, and precipitation enhancement. An important factor in all of these problems is the calculation of the plume trajectory, which is often complicated by the presence of multiple sources and water phase changes in the plume. The latent heat does not strongly influence plume rise if there is no cloud present at the top of the plume. A one dimensional plume and cloud growth model was developed to study these effects. In this paper, the predictions of the model are compared with observations of cooling tower plume rise at the John E. Amos, W. Va. fossil-fuel power plant

  7. Monte Carlo simulation techniques for predicting annual power production

    International Nuclear Information System (INIS)

    Cross, J.P.; Bulandr, P.J.

    1991-01-01

    As the owner and operator of a number of small to mid-sized hydroelectric sites, STS HydroPower has been faced with the need to accurately predict anticipated hydroelectric revenues over a period of years. The typical approach to this problem has been to look at each site from a mathematical deterministic perspective and evaluate the annual production from historic streamflows. Average annual production is simply taken to be the area under the flow duration curve defined by the operating and design characteristics of the selected turbines. Minimum annual production is taken to be a historic dry year scenario and maximum production is viewed as power generated under the most ideal of conditions. Such an approach creates two problems. First, in viewing the characteristics of a single site, it does not take into account the probability of such an event occurring. Second, in viewing all sites in a single organization's portfolio together, it does not reflect the varying flow conditions at the different sites. This paper attempts to address the first of these two concerns, that being the creation of a simulation model utilizing the Monte Carlo method at a single site. The result of the analysis is a picture of the production at the site that is both a better representation of anticipated conditions and defined probabilistically

  8. Predicting ice accretion and alleviating galloping on overhead power lines

    Science.gov (United States)

    Lu, Mingliang

    2002-04-01

    Both the static and dynamic effects of an ice storm on an overhead power line are investigated fairly comprehensively in this thesis. To determine the static, extreme ice load as well as the combined ice and wind load, a systematic procedure is established based on extensive freezing rain experiments and a Monte Carlo simulation. On the other hand, a dynamic effect---galloping---is examined quite extensively with the objective of better understanding its behavior. A novel add-on device---the hybrid nutation damper (HND)---is proposed to control galloping. Its effectiveness is assessed numerically by using a modified, 3DOF based, galloping software. The present investigations lead to the following findings. (i) Goodwin's simple theoretical model surprisingly predicts, quite accurately, the temporally changing weight of not only a dry ice growth but also a wet ice growth for a fixed, unheated conductor sample. (ii) The maximum ice loading may vary significantly over a power line's planned lifetime because of the randomness of an ice storm and its characteristics as well as the uncertainty involved in identifying the extreme probability distribution of the ice loading. Consequently, backup protection is presently essential for a power line in an ice prone area. (iii) A conductor's torsional flexibility does not appear to affect the growth of the accreted ice weight but it modifies the ice shape significantly. (iv) Three representative ice shapes (a crescent, D-like and icicle pendant) can initiate galloping so that galloping may occur in any icing condition. (v) A noticeable swingback or twist appears to develop only when their respective natural frequencies coincide with the plunge's natural frequency. (vi) A hydraulic jump is the major source of energy dissipation in a nutation damper. A properly induced rotation can significantly enhance a nutation damper's performance. (vii) A hybrid nutation damper has been demonstrated to be a promising means of alleviating

  9. Prediction of strong acceleration motion depended on focal mechanism; Shingen mechanism wo koryoshita jishindo yosoku ni tsuite

    Energy Technology Data Exchange (ETDEWEB)

    Kaneda, Y.; Ejiri, J. [Obayashi Corp., Tokyo (Japan)

    1996-10-01

    This paper describes simulation results of strong acceleration motion with varying uncertain fault parameters mainly for a fault model of Hyogo-ken Nanbu earthquake. For the analysis, based on the fault parameters, the strong acceleration motion was simulated using the radiation patterns and the breaking time difference of composite faults as parameters. A statistic waveform composition method was used for the simulation. For the theoretical radiation patterns, directivity was emphasized which depended on the strike of faults, and the maximum acceleration was more than 220 gal. While, for the homogeneous radiation patterns, the maximum accelerations were isotopically distributed around the fault as a center. For variations in the maximum acceleration and the predominant frequency due to the breaking time difference of three faults, the response spectral value of maximum/minimum was about 1.7 times. From the viewpoint of seismic disaster prevention, underground structures including potential faults and non-arranging properties can be grasped using this simulation. Significance of the prediction of strong acceleration motion was also provided through this simulation using uncertain factors, such as breaking time of composite faults, as parameters. 4 refs., 4 figs., 1 tab.

  10. Nonlocal response functions for predicting shear flow of strongly inhomogeneous fluids. I. Sinusoidally driven shear and sinusoidally driven inhomogeneity.

    Science.gov (United States)

    Glavatskiy, Kirill S; Dalton, Benjamin A; Daivis, Peter J; Todd, B D

    2015-06-01

    We present theoretical expressions for the density, strain rate, and shear pressure profiles in strongly inhomogeneous fluids undergoing steady shear flow with periodic boundary conditions. The expressions that we obtain take the form of truncated functional expansions. In these functional expansions, the independent variables are the spatially sinusoidal longitudinal and transverse forces that we apply in nonequilibrium molecular-dynamics simulations. The longitudinal force produces strong density inhomogeneity, and the transverse force produces sinusoidal shear. The functional expansions define new material properties, the response functions, which characterize the system's nonlocal response to the longitudinal force and the transverse force. We find that the sinusoidal longitudinal force, which is mainly responsible for the generation of density inhomogeneity, also modulates the strain rate and shear pressure profiles. Likewise, we find that the sinusoidal transverse force, which is mainly responsible for the generation of sinusoidal shear flow, can also modify the density. These cross couplings between density inhomogeneity and shear flow are also characterized by nonlocal response functions. We conduct nonequilibrium molecular-dynamics simulations to calculate all of the response functions needed to describe the response of the system for weak shear flow in the presence of strong density inhomogeneity up to the third order in the functional expansion. The response functions are then substituted directly into the truncated functional expansions and used to predict the density, velocity, and shear pressure profiles. The results are compared to the directly evaluated profiles from molecular-dynamics simulations, and we find that the predicted profiles from the truncated functional expansions are in excellent agreement with the directly computed density, velocity, and shear pressure profiles.

  11. Nonlocal response functions for predicting shear flow of strongly inhomogeneous fluids. I. Sinusoidally driven shear and sinusoidally driven inhomogeneity

    Science.gov (United States)

    Glavatskiy, Kirill S.; Dalton, Benjamin A.; Daivis, Peter J.; Todd, B. D.

    2015-06-01

    We present theoretical expressions for the density, strain rate, and shear pressure profiles in strongly inhomogeneous fluids undergoing steady shear flow with periodic boundary conditions. The expressions that we obtain take the form of truncated functional expansions. In these functional expansions, the independent variables are the spatially sinusoidal longitudinal and transverse forces that we apply in nonequilibrium molecular-dynamics simulations. The longitudinal force produces strong density inhomogeneity, and the transverse force produces sinusoidal shear. The functional expansions define new material properties, the response functions, which characterize the system's nonlocal response to the longitudinal force and the transverse force. We find that the sinusoidal longitudinal force, which is mainly responsible for the generation of density inhomogeneity, also modulates the strain rate and shear pressure profiles. Likewise, we find that the sinusoidal transverse force, which is mainly responsible for the generation of sinusoidal shear flow, can also modify the density. These cross couplings between density inhomogeneity and shear flow are also characterized by nonlocal response functions. We conduct nonequilibrium molecular-dynamics simulations to calculate all of the response functions needed to describe the response of the system for weak shear flow in the presence of strong density inhomogeneity up to the third order in the functional expansion. The response functions are then substituted directly into the truncated functional expansions and used to predict the density, velocity, and shear pressure profiles. The results are compared to the directly evaluated profiles from molecular-dynamics simulations, and we find that the predicted profiles from the truncated functional expansions are in excellent agreement with the directly computed density, velocity, and shear pressure profiles.

  12. Strong ground motion prediction applying dynamic rupture simulations for Beppu-Haneyama Active Fault Zone, southwestern Japan

    Science.gov (United States)

    Yoshimi, M.; Matsushima, S.; Ando, R.; Miyake, H.; Imanishi, K.; Hayashida, T.; Takenaka, H.; Suzuki, H.; Matsuyama, H.

    2017-12-01

    We conducted strong ground motion prediction for the active Beppu-Haneyama Fault zone (BHFZ), Kyushu island, southwestern Japan. Since the BHFZ runs through Oita and Beppy cities, strong ground motion as well as fault displacement may affect much to the cities.We constructed a 3-dimensional velocity structure of a sedimentary basin, Beppu bay basin, where the fault zone runs through and Oita and Beppu cities are located. Minimum shear wave velocity of the 3d model is 500 m/s. Additional 1-d structure is modeled for sites with softer sediment: holocene plain area. We observed, collected, and compiled data obtained from microtremor surveys, ground motion observations, boreholes etc. phase velocity and H/V ratio. Finer structure of the Oita Plain is modeled, as 250m-mesh model, with empirical relation among N-value, lithology, depth and Vs, using borehole data, then validated with the phase velocity data obtained by the dense microtremor array observation (Yoshimi et al., 2016).Synthetic ground motion has been calculated with a hybrid technique composed of a stochastic Green's function method (for HF wave), a 3D finite difference (LF wave) and 1D amplification calculation. Fault geometry has been determined based on reflection surveys and active fault map. The rake angles are calculated with a dynamic rupture simulation considering three fault segments under a stress filed estimated from source mechanism of earthquakes around the faults (Ando et al., JpGU-AGU2017). Fault parameters such as the average stress drop, a size of asperity etc. are determined based on an empirical relation proposed by Irikura and Miyake (2001). As a result, strong ground motion stronger than 100 cm/s is predicted in the hanging wall side of the Oita plain.This work is supported by the Comprehensive Research on the Beppu-Haneyama Fault Zone funded by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan.

  13. Empirical equations for the prediction of PGA and pseudo spectral accelerations using Iranian strong-motion data

    Science.gov (United States)

    Zafarani, H.; Luzi, Lucia; Lanzano, Giovanni; Soghrat, M. R.

    2018-01-01

    A recently compiled, comprehensive, and good-quality strong-motion database of the Iranian earthquakes has been used to develop local empirical equations for the prediction of peak ground acceleration (PGA) and 5%-damped pseudo-spectral accelerations (PSA) up to 4.0 s. The equations account for style of faulting and four site classes and use the horizontal distance from the surface projection of the rupture plane as a distance measure. The model predicts the geometric mean of horizontal components and the vertical-to-horizontal ratio. A total of 1551 free-field acceleration time histories recorded at distances of up to 200 km from 200 shallow earthquakes (depth regression analysis using the random effects algorithm of Abrahamson and Youngs (Bull Seism Soc Am 82:505-510, 1992), which considers between-events as well as within-events errors. Due to the limited data used in the development of previous Iranian ground motion prediction equations (GMPEs) and strong trade-offs between different terms of GMPEs, it is likely that the previously determined models might have less precision on their coefficients in comparison to the current study. The richer database of the current study allows improving on prior works by considering additional variables that could not previously be adequately constrained. Here, a functional form used by Boore and Atkinson (Earthquake Spect 24:99-138, 2008) and Bindi et al. (Bull Seism Soc Am 9:1899-1920, 2011) has been adopted that allows accounting for the saturation of ground motions at close distances. A regression has been also performed for the V/H in order to retrieve vertical components by scaling horizontal spectra. In order to take into account epistemic uncertainty, the new model can be used along with other appropriate GMPEs through a logic tree framework for seismic hazard assessment in Iran and Middle East region.

  14. Self-Conscious Shyness: Growth during Toddlerhood, Strong Role of Genetics, and No Prediction from Fearful Shyness.

    Science.gov (United States)

    Eggum-Wilkens, Natalie D; Lemery-Chalfant, Kathryn; Aksan, Nazan; Goldsmith, H Hill

    2015-01-01

    Fearful and self-conscious subtypes of shyness have received little attention in the empirical literature. Study aims included: 1) determining if fearful shyness predicted self-conscious shyness, 2) describing development of self-conscious shyness, and 3) examining genetic and environmental contributions to fearful and self-conscious shyness. Observed self-conscious shyness was examined at 19, 22, 25, and 28 months in same-sex twins (MZ = 102, DZ = 111, missing zygosity = 3 pairs). Self-conscious shyness increased across toddlerhood, but onset was earlier than predicted by theory. Fearful shyness (observed [6 and 12 months] and parents' reports [12 and 22 months]) was not predictive of self-conscious shyness. Independent genetic factors made strong contributions to parent-reported (but not observed) fearful shyness (additive genetic influence = .69 and .72 at 12 and 22 months, respectively) and self-conscious shyness (additive genetic influence = .90 for the growth model intercept). Results encourage future investigation of patterns of change and interrelations in shyness subtypes.

  15. Predictive Modeling for Strongly Correlated f-electron Systems: A first-principles and database driven machine learning approach

    Science.gov (United States)

    Ahmed, Towfiq; Khair, Adnan; Abdullah, Mueen; Harper, Heike; Eriksson, Olle; Wills, John; Zhu, Jian-Xin; Balatsky, Alexander

    Data driven computational tools are being developed for theoretical understanding of electronic properties in f-electron based materials, e.g., Lanthanides and Actnides compounds. Here we show our preliminary work on Ce compounds. Due to a complex interplay among the hybridization of f-electrons to non-interacting conduction band, spin-orbit coupling, and strong coulomb repulsion of f-electrons, no model or first-principles based theory can fully explain all the structural and functional phases of f-electron systems. Motivated by the large need in predictive modeling of actinide compounds, we adopted a data-driven approach. We found negative correlation between the hybridization and atomic volume. Mutual information between these two features were also investigated. In order to extend our search space with more features and predictability of new compounds, we are currently developing electronic structure database. Our f-electron database will be potentially aided by machine learning (ML) algorithm to extract complex electronic, magnetic and structural properties in f-electron system, and thus, will open up new pathways for predictive capabilities and design principles of complex materials. NSEC, IMS at LANL.

  16. Predicting long-term recovery of a strongly acidified stream using MAGIC and climate models (Litavka, Czech Republic

    Directory of Open Access Journals (Sweden)

    D. W. Hardekopf

    2008-03-01

    Full Text Available Two branches forming the headwaters of a stream in the Czech Republic were studied. Both streams have similar catchment characteristics and historical deposition; however one is rain-fed and strongly affected by acid atmospheric deposition, the other spring-fed and only moderately acidified. The MAGIC model was used to reconstruct past stream water and soil chemistry of the rain-fed branch, and predict future recovery up to 2050 under current proposed emissions levels. A future increase in air temperature calculated by a regional climate model was then used to derive climate-related scenarios to test possible factors affecting chemical recovery up to 2100. Macroinvertebrates were sampled from both branches, and differences in stream chemistry were reflected in the community structures. According to modelled forecasts, recovery of the rain-fed branch will be gradual and limited, and continued high levels of sulphate release from the soils will continue to dominate stream water chemistry, while scenarios related to a predicted increase in temperature will have little impact. The likelihood of colonization of species from the spring-fed branch was evaluated considering the predicted extent of chemical recovery. The results suggest that the possibility of colonization of species from the spring-fed branch to the rain-fed will be limited to only the acid-tolerant stonefly, caddisfly and dipteran taxa in the modelled period.

  17. Wind turbine power curve prediction with consideration of rotational augmentation effects

    Science.gov (United States)

    Tang, X.; Huang, X.; Sun, S.; Peng, R.

    2016-11-01

    Wind turbine power curve expresses the relationship between the rotor power and the hub wind speed. Wind turbine power curve prediction is of vital importance for power control and wind energy management. To predict power curve, the Blade Element Moment (BEM) method is used in both academic and industrial communities. Due to the limited range of angles of attack measured in wind tunnel testing and the three-dimensional (3D) rotational augmentation effects in rotating turbines, wind turbine power curve prediction remains a challenge especially at high wind speeds. This paper presents an investigation of considering the rotational augmentation effects using characterized lift and drag coefficients from 3D computational fluid dynamics (CFD) simulations coupled in the BEM method. A Matlab code was developed to implement the numerical calculation. The predicted power outputs were compared with the NREL Phase VI wind turbine measurements. The results demonstrate that the coupled method improves the wind turbine power curve prediction.

  18. Very short-term spatio-temporal wind power prediction using a censored Gaussian field

    DEFF Research Database (Denmark)

    Baxevani, Anastassia; Lenzi, Amanda

    2018-01-01

    to predict the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. We aim at contributing to the literature of wind power prediction by developing and analysing a spatio...

  19. Replicability and 40-Year Predictive Power of Childhood ARC Types

    Science.gov (United States)

    Chapman, Benjamin P.; Goldberg, Lewis R.

    2011-01-01

    We examined three questions surrounding the Undercontrolled, Overcontrolled, and Resilient--or Asendorpf-Robins-Caspi (ARC)--personality types originally identified by Block (1971). In analyses of the teacher personality assessments of over 2,000 children in 1st through 6th grade in 1959-1967, and follow-up data on general and cardiovascular health outcomes in over 1,100 adults recontacted 40 years later, we found: (1) Bootstrapped internal replication clustering suggested that Big Five scores were best characterized by a tripartite cluster structure corresponding to the ARC types; (2) this cluster structure was fuzzy, rather than discrete, indicating that ARC constructs are best represented as gradients of similarity to three prototype Big Five profiles; and (3) ARC types and degrees of ARC prototypicality showed associations with multiple health outcomes 40 years later. ARC constructs were more parsimonious, but neither better nor more consistent predictors than the dimensional Big Five traits. Forty-year incident cases of heart disease could be correctly identified with 68% accuracy by personality information alone, a figure approaching the 12-year accuracy of a leading medical cardiovascular risk model. Findings support the theoretical validity of ARC constructs, their treatment as continua of prototypicality rather than discrete categories, and the need for further understanding the robust predictive power of childhood personality traits for mid-life health. PMID:21744975

  20. Constraints on a possible evolution of mass density power-law index in strong gravitational lensing from cosmological data

    Science.gov (United States)

    Holanda, R. F. L.; Pereira, S. H.; Jain, Deepak

    2017-11-01

    In this work, by using strong gravitational lensing (SGL) observations along with Type Ia Supernovae (Union2.1) and gamma-ray burst data (GRBs), we propose a new method to study a possible redshift evolution of γ(z), the mass density power-law index of strong gravitational lensing systems. In this analysis, we assume the validity of cosmic distance duality relation and the flat universe. In order to explore the γ(z) behaviour, three different parametrizations are considered, namely: (P1) γ(zl) = γ0 + γ1zl; (P2) γ(zl) = γ0 + γ1zl/(1 + zl); and (P3) γ(zl) = γ0 + γ1ln (1 + zl), where zl corresponds to lens redshift. If γ0 = 2 and γ1 = 0, the singular isothermal sphere model is recovered. Our method is performed on SGL sub-samples defined by different lens redshifts and velocity dispersions. For the former case, the results are in full agreement with each other, while a 1σ tension between the sub-samples with low (≤250 km s-1) and high (>250 km s-1) velocity dispersions was obtained on the (γ0-γ1) plane. By considering the complete SGL sample, we obtain γ0 ≈ 2 and γ1 ≈ 0 within 1σ c.l. for all γ(z) parametrizations. However, we find the following best-fitting values of γ1: -0.085; -0.16; and -0.12 for P1, P2 and P3 parametrizations, respectively, suggesting a mild evolution for γ(z). By repeating the analysis with Type Ia Supernovae from Joint Light Analysis compilation, GRBs and SGL systems this mild evolution is reinforced.

  1. Power capability prediction for lithium-ion batteries based on multiple constraints analysis

    International Nuclear Information System (INIS)

    Pan, Rui; Wang, Yujie; Zhang, Xu; Yang, Duo; Chen, Zonghai

    2017-01-01

    Highlights: • Multiple constraints for peak power capability prediction are deeply analyzed. • Multi-limited method is proposed for the peak power capability prediction of LIBs. • The EKF is used for the model based peak power capability prediction. • The FUDS and UDDS profiles are executed to evaluate the proposed method. - Abstract: The power capability of the lithium-ion battery is a key performance indicator for electric vehicle, and it is intimately correlated with the acceleration, regenerative braking and gradient climbing power requirements. Therefore, an accurate power capability or state-of-power prediction is critical to a battery management system, which can help the battery to work in suitable area and prevent the battery from over-charging and over-discharging. However, the power capability is easily affected by dynamic load, voltage variation and temperature. In this paper, three different constraints in power capability prediction are introduced, and the advantages and disadvantages of the three methods are deeply analyzed. Furthermore, a multi-limited approach for the power capability prediction is proposed, which can overcome the drawbacks of the three methods. Subsequently, the extended Kalman filter algorithm is employed for model based state-of-power prediction. In order to verify the proposed method, diverse experiments are executed to explore the efficiency, robustness, and precision. The results indicate that the proposed method can improve the precision and robustness obviously.

  2. State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations

    OpenAIRE

    Guoxu Wang; Jie Wu; Bifan Zeng; Zhibin Xu; Wanqiang Wu; Xiaoqian Ma

    2017-01-01

    A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reacto...

  3. A Wind Power and Load Prediction Based Frequency Control Approach for Wind-Diesel-Battery Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Chao Peng

    2015-01-01

    Full Text Available A frequency control approach based on wind power and load power prediction information is proposed for wind-diesel-battery hybrid power system (WDBHPS. To maintain the frequency stability by wind power and diesel generation as much as possible, a fuzzy control theory based wind and diesel power control module is designed according to wind power and load prediction information. To compensate frequency fluctuation in real time and enhance system disturbance rejection ability, a battery energy storage system real-time control module is designed based on ADRC (active disturbance rejection control. The simulation experiment results demonstrate that the proposed approach has a better disturbance rejection ability and frequency control performance compared with the traditional droop control approach.

  4. Survey of strong motion earthquake effects on thermal power plants in California with emphasis on piping systems. Volume 1, Main report

    International Nuclear Information System (INIS)

    Stevenson, J.D.

    1995-11-01

    Since 1982, there has been a major effort expended to evaluate the susceptibility of nuclear Power plant equipment to failure and significant damage during seismic events. This was done by making use of data on the performance of electrical and mechanical equipment in conventional power plants and other similar industrial facilities during strong motion earthquakes. This report is intended as an extension of the seismic experience data collection effort and a compilation of experience data specific to power plant piping and supports designed and constructed US power piping code requirements which have experienced strong motion earthquakes. Eight damaging (Richter Magnitude 7.7 to 5.5) California earthquakes and their effects on 8 power generating facilities in use natural gas and California were reviewed. All of these facilities were visited and evaluated. Seven fossel-fueled (dual use natural gas and oil) and one nuclear fueled plants consisting of a total of 36 individual boiler or reactor units were investigated. Peak horizontal ground accelerations that either had been recorded on site at these facilities or were considered applicable to these power plants on the basis of nearby recordings ranged between 0.20g and 0.5lg with strong motion durations which varied from 3.5 to 15 seconds. Most US nuclear power plants are designed for a safe shutdown earthquake peak ground acceleration equal to 0.20g or less with strong motion durations which vary from 10 to 15 seconds

  5. Fuzzy-predictive direct power control implementation of a grid connected photovoltaic system, associated with an active power filter

    International Nuclear Information System (INIS)

    Ouchen, Sabir; Betka, Achour; Abdeddaim, Sabrina; Menadi, Abdelkrim

    2016-01-01

    Highlights: • An implementation on dSPACE 1104 of a double stage grid connected photovoltaic system, associated with an active power filter. • A fuzzy logic controller for maximum power point tracking of photovoltaic generator using a boost converter. • Predictive direct power control almost eliminates the effect of harmonics under a unite power factor. • The robustness of control strategies was examined in different irradiance level conditions. - Abstract: The present paper proposes a real time implementation of an optimal operation of a double stage grid connected photovoltaic system, associated with a shunt active power filter. On the photovoltaic side, a fuzzy logic based maximum power point taking control is proposed to track permanently the optimum point through an adequate tuning of a boost converter regardless the solar irradiance variations; whereas, on the grid side, a model predictive direct power control is applied, to ensure both supplying a part of the load demand with the extracted photovoltaic power, and a compensation of undesirable harmonic contents of the grid current, under a unity power factor operation. The implementation of the control strategies is conducted on a small scale photovoltaic system, controlled via a dSPACE 1104 single card. The obtained experimental results show on one hand, that the proposed Fuzzy logic based maximum power taking point technique provides fast and high performances under different irradiance levels while compared with a sliding mode control, and ensures 1.57% more in efficiency. On the other hand, the predictive power control ensures a flexible settlement of active power amounts exchanges with the grid, under a unity power functioning. Furthermore, the grid current presents a sinusoidal shape with a tolerable total harmonic distortion coefficient 4.71%.

  6. Predictive Smart Grid Control with Exact Aggregated Power Constraints

    DEFF Research Database (Denmark)

    Trangbæk, K; Petersen, Mette Højgaard; Bendtsen, Jan Dimon

    2012-01-01

    consumption, and on the other hand from natural variations in power production from e.g. wind turbines. The consumers represent energy-consuming units such as heat pumps, car batteries etc. These units obviously have limits on how much power and energy they can consume at any given time, which impose...

  7. Predicting Rediated Noise With Power Flow Finite Element Analysis

    Science.gov (United States)

    2007-02-01

    vibratoire est traité d’une manière analogue au traitement du flux d’énergie thermique en régime stationnaire. RDDC a travaillé pendant plusieurs années à...5 Figure 2.3 SNAP Predictions of Transfer Mobility in Shell....................................................... 5 Figure 2.4...Comparison of SNAP and Experimental Predictions of Transfer Mobility in Shell 6 Figure 2.5 Comparison of SNAP and Experimental Predictions of Transfer

  8. A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm

    Directory of Open Access Journals (Sweden)

    Jidong Wang

    2017-04-01

    Full Text Available With the increasing proportion of photovoltaic (PV power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM, which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems.

  9. Reactive Power Impact on Lifetime Prediction of Two-level Wind Power Converter

    DEFF Research Database (Denmark)

    Zhou, Dao; Blaabjerg, Frede; Lau, M.

    2013-01-01

    The influence of reactive power injection on the dominating two-level wind power converter is investigated and compared in terms of power loss and thermal behavior. Then the lifetime of both the partial-scale and full-scale power converter is estimated based on the widely used Coffin-Manson model...

  10. Short-term wind power prediction based on LSSVM–GSA model

    International Nuclear Information System (INIS)

    Yuan, Xiaohui; Chen, Chen; Yuan, Yanbin; Huang, Yuehua; Tan, Qingxiong

    2015-01-01

    Highlights: • A hybrid model is developed for short-term wind power prediction. • The model is based on LSSVM and gravitational search algorithm. • Gravitational search algorithm is used to optimize parameters of LSSVM. • Effect of different kernel function of LSSVM on wind power prediction is discussed. • Comparative studies show that prediction accuracy of wind power is improved. - Abstract: Wind power forecasting can improve the economical and technical integration of wind energy into the existing electricity grid. Due to its intermittency and randomness, it is hard to forecast wind power accurately. For the purpose of utilizing wind power to the utmost extent, it is very important to make an accurate prediction of the output power of a wind farm under the premise of guaranteeing the security and the stability of the operation of the power system. In this paper, a hybrid model (LSSVM–GSA) based on the least squares support vector machine (LSSVM) and gravitational search algorithm (GSA) is proposed to forecast the short-term wind power. As the kernel function and the related parameters of the LSSVM have a great influence on the performance of the prediction model, the paper establishes LSSVM model based on different kernel functions for short-term wind power prediction. And then an optimal kernel function is determined and the parameters of the LSSVM model are optimized by using GSA. Compared with the Back Propagation (BP) neural network and support vector machine (SVM) model, the simulation results show that the hybrid LSSVM–GSA model based on exponential radial basis kernel function and GSA has higher accuracy for short-term wind power prediction. Therefore, the proposed LSSVM–GSA is a better model for short-term wind power prediction

  11. Right Heart End-Systolic Remodeling Index Strongly Predicts Outcomes in Pulmonary Arterial Hypertension: Comparison With Validated Models.

    Science.gov (United States)

    Amsallem, Myriam; Sweatt, Andrew J; Aymami, Marie C; Kuznetsova, Tatiana; Selej, Mona; Lu, HongQuan; Mercier, Olaf; Fadel, Elie; Schnittger, Ingela; McConnell, Michael V; Rabinovitch, Marlene; Zamanian, Roham T; Haddad, Francois

    2017-06-01

    Right ventricular (RV) end-systolic dimensions provide information on both size and function. We investigated whether an internally scaled index of end-systolic dimension is incremental to well-validated prognostic scores in pulmonary arterial hypertension. From 2005 to 2014, 228 patients with pulmonary arterial hypertension were prospectively enrolled. RV end-systolic remodeling index (RVESRI) was defined by lateral length divided by septal height. The incremental values of RV free wall longitudinal strain and RVESRI to risk scores were determined. Mean age was 49±14 years, 78% were female, 33% had connective tissue disease, 52% were in New York Heart Association class ≥III, and mean pulmonary vascular resistance was 11.2±6.4 WU. RVESRI and right atrial area were strongly connected to the other right heart metrics. Three zones of adaptation (adapted, maladapted, and severely maladapted) were identified based on the RVESRI to RV systolic pressure relationship. During a mean follow-up of 3.9±2.4 years, the primary end point of death, transplant, or admission for heart failure was reached in 88 patients. RVESRI was incremental to risk prediction scores in pulmonary arterial hypertension, including the Registry to Evaluate Early and Long-Term PAH Disease Management score, the Pulmonary Hypertension Connection equation, and the Mayo Clinic model. Using multivariable analysis, New York Heart Association class III/IV, RVESRI, and log NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) were retained (χ 2 , 62.2; P right heart metrics, RVESRI demonstrated the best test-retest characteristics. RVESRI is a simple reproducible prognostic marker in patients with pulmonary arterial hypertension. © 2017 American Heart Association, Inc.

  12. Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

    Energy Technology Data Exchange (ETDEWEB)

    Melin, Alexander M. [ORNL; Olama, Mohammed M. [ORNL; Dong, Jin [ORNL; Djouadi, Seddik M. [ORNL; Zhang, Yichen [University of Tennessee, Knoxville (UTK), Department of Electrical Engineering and Computer Science

    2017-09-01

    The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed to estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.

  13. Utilizing the Elements of National Power to Predict Ungoverned Space

    Science.gov (United States)

    2007-05-07

    Military Review (November-December 2006): 51-57. Chomsky , Noam . Failed States The Abuse of Power and Assault on Democracy. New York, NY: Henry Holt...each nation in a region by assessing the various categories of their national power. This information allows commanders to make educated decisions on...GDP of a nation. Countries that do not invest in the education of their citizens remain below the linear regression and are unable to increase

  14. Using dynamical uncertainty models estimating uncertainty bounds on power plant performance prediction

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Mataji, B.

    2007-01-01

    of the prediction error. These proposed dynamical uncertainty models result in an upper and lower bound on the predicted performance of the plant. The dynamical uncertainty models are used to estimate the uncertainty of the predicted performance of a coal-fired power plant. The proposed scheme, which uses dynamical...

  15. Optimization of maintenance for power system equipment using a predictive health model

    NARCIS (Netherlands)

    Bajracharya, G.; Koltunowicz, T.; Negenborn, R.R.; Papp, Z.; Djairam, D.; Schutter, B.D. de; Smit, J.J.

    2009-01-01

    In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based

  16. Modelling of physical properties - databases, uncertainties and predictive power

    DEFF Research Database (Denmark)

    Gani, Rafiqul

    Physical and thermodynamic property in the form of raw data or estimated values for pure compounds and mixtures are important pre-requisites for performing tasks such as, process design, simulation and optimization; computer aided molecular/mixture (product) design; and, product-process analysis...... in the estimated/predicted property values, how to assess the quality and reliability of the estimated/predicted property values? The paper will review a class of models for prediction of physical and thermodynamic properties of organic chemicals and their mixtures based on the combined group contribution – atom...

  17. A study on the characteristics, predictions and policies of China’s eight main power grids

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Dong, Yao; Jiang, He

    2014-01-01

    Highlights: • Indian blackout is analyzed as a warning for China’s power system. • Issues and recommendations of China’s eight power grids are presented. • Five models are employed for scenario analysis on power generation and consumption. • The optimized combined model outperforms other models. • Methods towards balancing power generation and environmental impacts are proposed. - Abstract: Electricity is an indispensable energy source for modern social and economic development. However, large-scale blackouts can cause incalculable loss to society. In 2012, three major Indian power grids collapsed, resulting in the interruption of the electricity supply to over 600 million people. To avoid an event like that, China needs to forecast the power generation and consumption of eight power grids effectively. This paper first analyzes the characteristics of eight power grids and then proposes a combined model based on three improved grey models optimized by a differential evolution algorithm to predict electricity production and consumption of each power grid. The optimized combined forecasting model provides a better prediction than other models, and it is also the most workable and satisfactory model. Experiment results show electricity production and consumption would increase. In consideration of the real situation and existing problems, some suggestions are proposed. The government could decrease thermal power and exploit renewable energy power, like hydroelectric power, wind power and solar power, to ensure the safe and reliable operation of China’s major power grids and protect environment

  18. Fuzzy model predictive control algorithm applied in nuclear power plant

    International Nuclear Information System (INIS)

    Zuheir, Ahmad

    2006-01-01

    The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

  19. Prediction on corrosion rate of pipe in nuclear power system based on optimized grey theory

    International Nuclear Information System (INIS)

    Chen Yonghong; Zhang Dafa; Chen Dengke; Jiang Wei

    2007-01-01

    For the prediction of corrosion rate of pipe in nuclear power system, the pre- diction error from the grey theory is greater, so a new method, optimized grey theory was presented in the paper. A comparison among predicted results from present and other methods was carried out, and it is seem that optimized grey theory is correct and effective for the prediction of corrosion rate of pipe in nuclear power system, and it provides a fundamental basis for the maintenance of pipe in nuclear power system. (authors)

  20. Cutting power prediction model for turning of GFRP composites ...

    African Journals Online (AJOL)

    Glass fiber reinforced plastic (GFRP) composite materials are replacing traditional engineering materials owing to their superior properties. Accordingly, the need for accurate machining of composites has increased enormously. This paper deals with the study of power consumption in machining of GFRP composite tubes of ...

  1. Seismic rupture modelling, strong motion prediction and seismic hazard assessment: fundamental and applied approaches; Modelisation de la rupture sismique, prediction du mouvement fort, et evaluation de l'alea sismique: approches fondamentale et appliquee

    Energy Technology Data Exchange (ETDEWEB)

    Berge-Thierry, C

    2007-05-15

    The defence to obtain the 'Habilitation a Diriger des Recherches' is a synthesis of the research work performed since the end of my Ph D. thesis in 1997. This synthesis covers the two years as post doctoral researcher at the Bureau d'Evaluation des Risques Sismiques at the Institut de Protection (BERSSIN), and the seven consecutive years as seismologist and head of the BERSSIN team. This work and the research project are presented in the framework of the seismic risk topic, and particularly with respect to the seismic hazard assessment. Seismic risk combines seismic hazard and vulnerability. Vulnerability combines the strength of building structures and the human and economical consequences in case of structural failure. Seismic hazard is usually defined in terms of plausible seismic motion (soil acceleration or velocity) in a site for a given time period. Either for the regulatory context or the structural specificity (conventional structure or high risk construction), seismic hazard assessment needs: to identify and locate the seismic sources (zones or faults), to characterize their activity, to evaluate the seismic motion to which the structure has to resist (including the site effects). I specialized in the field of numerical strong-motion prediction using high frequency seismic sources modelling and forming part of the IRSN allowed me to rapidly working on the different tasks of seismic hazard assessment. Thanks to the expertise practice and the participation to the regulation evolution (nuclear power plants, conventional and chemical structures), I have been able to work on empirical strong-motion prediction, including site effects. Specific questions related to the interface between seismologists and structural engineers are also presented, especially the quantification of uncertainties. This is part of the research work initiated to improve the selection of the input ground motion in designing or verifying the stability of structures. (author)

  2. Basic study on dynamic reactive-power control method with PV output prediction for solar inverter

    Directory of Open Access Journals (Sweden)

    Ryunosuke Miyoshi

    2016-01-01

    Full Text Available To effectively utilize a photovoltaic (PV system, reactive-power control methods for solar inverters have been considered. Among the various methods, the constant-voltage control outputs less reactive power compared with the other methods. We have developed a constant-voltage control to reduce the reactive-power output. However, the developed constant-voltage control still outputs unnecessary reactive power because the control parameter is constant in every waveform of the PV output. To reduce the reactive-power output, we propose a dynamic reactive-power control method with a PV output prediction. In the proposed method, the control parameter is varied according to the properties of the predicted PV waveform. In this study, we performed numerical simulations using a distribution system model, and we confirmed that the proposed method reduces the reactive-power output within the voltage constraint.

  3. Sludge pipe flow pressure drop prediction using composite power ...

    African Journals Online (AJOL)

    2011-09-30

    Sep 30, 2011 ... When predicting pressure gradients for the flow of sludges in pipes, the rheology of the fluid plays an important role, especially with increasing concentration of the suspended matter in the sludge. The f-Re relationship is often applied when designing pipelines, but it depends on the rheological parameters ...

  4. Predicting norm enforcement: The individual and joint predictive power of economic preferences, personality, and self-control

    OpenAIRE

    Friehe, Tim; Schildberg-Hörisch, Hannah

    2017-01-01

    This paper explores the individual and joint predictive power of concepts from economics, psychology, and criminology for individual norm enforcement behavior. More specifically, we consider economic preferences (patience and attitudes towards risk), personality traits from psychology (Big Five and locus of control), and a self-control scale from criminology. Using survey data, we show that the various concepts complement each other in predicting self-reported norm enforcement behavior. The m...

  5. Designing Predictive Diagnose Method for Insulation Resistance Degradation of the Electrical Power Cables from Neutral Insulated Power Networks

    Science.gov (United States)

    Dobra, R.; Pasculescu, D.; Risteiu, M.; Buica, G.; Jevremović, V.

    2017-06-01

    This paper describe some possibilities to minimize voltages switching-off risks from the mining power networks, in case of insulated resistance faults by using a predictive diagnose method. The cables from the neutral insulated power networks (underground mining) are designed to provide a flexible electrical connection between portable or mobile equipment and a point of supply, including main feeder cable for continuous miners, pump cable, and power supply cable. An electronic protection for insulated resistance of mining power cables can be made using this predictive strategy. The main role of electronic relays for insulation resistance degradation of the electrical power cables, from neutral insulated power networks, is to provide a permanent measurement of the insulated resistance between phases and ground, in order to switch-off voltage when the resistance value is below a standard value. The automat system of protection is able to signalize the failure and the human operator will be early informed about the switch-off power and will have time to take proper measures to fix the failure. This logic for fast and automat switch-off voltage without aprioristic announcement is suitable for the electrical installations, realizing so a protection against fires and explosion. It is presented an algorithm and an anticipative relay for insulated resistance control from three-phase low voltage installations with insulated neutral connection.

  6. Application of bounding spectra to seismic design of piping based on the performance of above ground piping in power plants subjected to strong motion earthquakes

    Energy Technology Data Exchange (ETDEWEB)

    Stevenson, J.D. [Stevenson and Associates, Cleveland, OH (United States)

    1995-02-01

    This report extends the potential application of Bounding Spectra evaluation procedures, developed as part of the A-46 Unresolved Safety Issue applicable to seismic verification of in-situ electrical and mechanical equipment, to in-situ safety related piping in nuclear power plants. The report presents a summary of earthquake experience data which define the behavior of typical U.S. power plant piping subject to strong motion earthquakes. The report defines those piping system caveats which would assure the seismic adequacy of the piping systems which meet those caveats and whose seismic demand are within the bounding spectra input. Based on the observed behavior of piping in strong motion earthquakes, the report describes the capabilities of the piping system to carry seismic loads as a function of the type of connection (i.e. threaded versus welded). This report also discusses in some detail the basic causes and mechanisms for earthquake damages and failures to power plant piping systems.

  7. Application of bounding spectra to seismic design of piping based on the performance of above ground piping in power plants subjected to strong motion earthquakes

    International Nuclear Information System (INIS)

    Stevenson, J.D.

    1995-02-01

    This report extends the potential application of Bounding Spectra evaluation procedures, developed as part of the A-46 Unresolved Safety Issue applicable to seismic verification of in-situ electrical and mechanical equipment, to in-situ safety related piping in nuclear power plants. The report presents a summary of earthquake experience data which define the behavior of typical U.S. power plant piping subject to strong motion earthquakes. The report defines those piping system caveats which would assure the seismic adequacy of the piping systems which meet those caveats and whose seismic demand are within the bounding spectra input. Based on the observed behavior of piping in strong motion earthquakes, the report describes the capabilities of the piping system to carry seismic loads as a function of the type of connection (i.e. threaded versus welded). This report also discusses in some detail the basic causes and mechanisms for earthquake damages and failures to power plant piping systems

  8. Predictive & Prognostic Controller for Wide Band Gap (Silicon Carbide) Power Conversion (Preprint)

    National Research Council Canada - National Science Library

    Davis, Gregg; Casey, Leo; Jordan, Brett; Scofield, Jim; Keller, Kirby; Sheahan, Jim; Roach, Jeffrey; Scherrer, Michael; Singh, Ranbir

    2006-01-01

    This report was developed under a SBIR contract. This paper presents an approach to predictive control and prognostication intended to increase the confidence levels for power converters in aerospace applications...

  9. Research on power grid loss prediction model based on Granger causality property of time series

    Energy Technology Data Exchange (ETDEWEB)

    Wang, J. [North China Electric Power Univ., Beijing (China); State Grid Corp., Beijing (China); Yan, W.P.; Yuan, J. [North China Electric Power Univ., Beijing (China); Xu, H.M.; Wang, X.L. [State Grid Information and Telecommunications Corp., Beijing (China)

    2009-03-11

    This paper described a method of predicting power transmission line losses using the Granger causality property of time series. The stable property of the time series was investigated using unit root tests. The Granger causality relationship between line losses and other variables was then determined. Granger-caused time series were then used to create the following 3 prediction models: (1) a model based on line loss binomials that used electricity sales to predict variables, (2) a model that considered both power sales and grid capacity, and (3) a model based on autoregressive distributed lag (ARDL) approaches that incorporated both power sales and the square of power sales as variables. A case study of data from China's electric power grid between 1980 and 2008 was used to evaluate model performance. Results of the study showed that the model error rates ranged between 2.7 and 3.9 percent. 6 refs., 3 tabs., 1 fig.

  10. Out-of-step Prediction for Power System Using Improved Prony Algorithm

    Directory of Open Access Journals (Sweden)

    Shi Fang

    2016-01-01

    Full Text Available The development of the phasor measurement units (PMUs and the wide-area measurement systems (WAMS in power system provide abundant synchronous data for the new application. This paper proposes an online out-of-step monitoring and predicting scheme for the interconnected power systems based on the rotor angle measurement. The Prony algorithm is improved to predict whether and when a power swing will lead to out-of-step of the generator or the power systems. An energy-like weight coefficient approximation of the decomposed signals is introduced to maintain the accuracy and reliability of the model free dynamic prediction method. The effectiveness and applicability of the proposed scheme is illustrated using a 2-area-4-machine power system.

  11. Prediction of speech intelligibility based on a correlation metric in the envelope power spectrum domain

    DEFF Research Database (Denmark)

    Relano-Iborra, Helia; May, Tobias; Zaar, Johannes

    A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speechbased envelope power spectrum model (sEPSMcorr) [1], based on the auditory processing of the multi-resolution speech-based Envel......A powerful tool to investigate speech perception is the use of speech intelligibility prediction models. Recently, a model was presented, termed correlation-based speechbased envelope power spectrum model (sEPSMcorr) [1], based on the auditory processing of the multi-resolution speech......-based Envelope Power Spectrum Model (mr-sEPSM) [2], combined with the correlation back-end of the Short-Time Objective Intelligibility measure (STOI) [3]. The sEPSMcorr can accurately predict NH data for a broad range of listening conditions, e.g., additive noise, phase jitter and ideal binary mask processing....

  12. Wind power price trends in the United States: Struggling to remain competitive in the face of strong growth

    International Nuclear Information System (INIS)

    Bolinger, Mark; Wiser, Ryan

    2009-01-01

    The amount of wind power capacity being installed globally is surging, with the United States the world leader in terms of annual market share for three years running (2005-2007). The rapidly growing market for wind has been a double-edged sword, however, as the resulting supply-demand imbalance in wind turbines, along with the rising cost of materials and weakness in the US dollar, has put upward pressure on wind turbine costs, and ultimately, wind power prices. Two mitigating factors-reductions in the cost of equity provided to wind projects and improvements in project-level capacity factors-have helped to relieve some of the upward pressure on wind power prices over the last few years. Because neither of these two factors can be relied upon to further cushion the blow going forward, policymakers should recognize that continued financial support may be necessary to sustain the wind sector at its current pace of development, at least in the near term. Though this article emphasizes developments in the US market for wind power, those trends are similar to, and hold implications for, the worldwide wind power market

  13. Improving nuclear power plant reliability through predictive maintenance

    International Nuclear Information System (INIS)

    Geilhausen, R.; Kunze, U.

    1996-01-01

    Maintenance strategies can be assigned to one of three categories: failure maintenance, periodic maintenance or condition-oriented maintenance. The optimum maintenance scheme can be selected on the basis of a cost-benefit analysis but the safety of life and limb or the political climate for NPP can hardly expressed in numbers. The implementation of preventive maintenance needs two preconditions: high-performance instrumentation in the form of stationary and mobile monitoring systems for the determination of the condition of the nuclear power plant components and provision of a tool that can handle both the organization of the work and the evaluation of the results obtained. (authors)

  14. On-line test of power distribution prediction system for boiling water reactors

    International Nuclear Information System (INIS)

    Nishizawa, Y.; Kiguchi, T.; Kobayashi, S.; Takumi, K.; Tanaka, H.; Tsutsumi, R.; Yokomi, M.

    1982-01-01

    A power distribution prediction system for boiling water reactors has been developed and its on-line performance test has proceeded at an operating commercial reactor. This system predicts the power distribution or thermal margin in advance of control rod operations and core flow rate change. This system consists of an on-line computer system, an operator's console with a color cathode-ray tube, and plant data input devices. The main functions of this system are present power distribution monitoring, power distribution prediction, and power-up trajectory prediction. The calculation method is based on a simplified nuclear thermal-hydraulic calculation, which is combined with a method of model identification to the actual reactor core state. It has been ascertained by the on-line test that the predicted power distribution (readings of traversing in-core probe) agrees with the measured data within 6% root-mean-square. The computing time required for one prediction calculation step is less than or equal to 1.5 min by an HIDIC-80 on-line computer

  15. The predictive power of Japanese candlestick charting in Chinese stock market

    Science.gov (United States)

    Chen, Shi; Bao, Si; Zhou, Yu

    2016-09-01

    This paper studies the predictive power of 4 popular pairs of two-day bullish and bearish Japanese candlestick patterns in Chinese stock market. Based on Morris' study, we give the quantitative details of definition of long candlestick, which is important in two-day candlestick pattern recognition but ignored by several previous researches, and we further give the quantitative definitions of these four pairs of two-day candlestick patterns. To test the predictive power of candlestick patterns on short-term price movement, we propose the definition of daily average return to alleviate the impact of correlation among stocks' overlap-time returns in statistical tests. To show the robustness of our result, two methods of trend definition are used for both the medium-market-value and large-market-value sample sets. We use Step-SPA test to correct for data snooping bias. Statistical results show that the predictive power differs from pattern to pattern, three of the eight patterns provide both short-term and relatively long-term prediction, another one pair only provide significant forecasting power within very short-term period, while the rest three patterns present contradictory results for different market value groups. For all the four pairs, the predictive power drops as predicting time increases, and forecasting power is stronger for stocks with medium market value than those with large market value.

  16. THE PREDICTIVE POWER OF RELIGIOUS ORIENTATION TYPES ON AMBIVALENT SEXISM

    Directory of Open Access Journals (Sweden)

    Fatih Ozdemir

    2016-07-01

    Full Text Available The purpose of the present study was to predict ambivalent sexism (including hostile sexism and benevolent sexism with religious orientation types as intrinsic religiosity, extrinsic religiosity and quest religiosity. In addition, the effect of demographic variables (including age, gender, education on sexist attitudes was tested. 583 (N_female= 318; N_male= 265 university students who study in different universities of Ankara/Turkey (M_age= 22.10; SD = 2.33 completed Ambivalent Sexism Inventory, and Religious Orientation Scale. Findings indicated significant gender differences on study variables and significant associations between ambivalent sexism and religious orientation types within university students sample in Turkey.

  17. Critical assessment of indoor noise propagation and prediction in power plants

    Science.gov (United States)

    Brittain, Frank H.

    2005-09-01

    Accurate prediction of indoor noise propagation in power plants is important to help estimate occupational noise exposures, and to help predict community noise radiated by plant walls-from levels predicted just inside of each wall. Unfortunately, the basic theories of room acoustics are not applicable. Most power plant rooms are both too large, and too odd shaped for basic room theory, including the Sabine and Norris-Erying theories, to be applicable. Even more important, basic room theory requires empty rooms, and power plant spaces are densely packed with equipment, piping, cable trays, etc. (called fittings). This paper reviews basic room theory, and outlines deficiencies for use in predicting noise propagation inside power plant buildings. Examples are given of walk-away measurements showing that there is no reverberant field, and that reverberation measurements do not correlate well with walk-away test data. Using measurements as an alternative to levels predicted just inside of plant walls to help predict community noise radiated by each wall are discussed. Software for predicting noise in industrial spaces is identified, and their suitability for power plants, which have unusually high fitting densities, is also discussed.

  18. Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

    DEFF Research Database (Denmark)

    Preindl, Matthias; Schaltz, Erik

    2011-01-01

    The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive...

  19. Distributed Model Predictive Control for Active Power Control of Wind Farm

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Rasmussen, Claus Nygaard

    2014-01-01

    This paper presents the active power control of a wind farm using the Distributed Model Predictive Controller (D- MPC) via dual decomposition. Different from the conventional centralized wind farm control, multiple objectives such as power reference tracking performance and wind turbine load can......-scale wind farm control....

  20. Influence of a strong laser field on Coulomb explosion and stopping power of energetic H3+ clusters in plasmas

    International Nuclear Information System (INIS)

    Wang Guiqiu; Gao Hong; Wang Yaochuan; Yao Li; Zhong Haiyang; Cheng Lihong; Yang Kun; Liu Wei; E Peng; Xu Dianguo; Wang Younian; Hu Zhanghu

    2012-01-01

    The influence of a high-intensity laser field on the Coulomb explosion and stopping power for a swift H 3 + cluster ion in a plasma target is studied by means of the molecular dynamic (MD) method based on the linearized Vlasov–Poisson theory. Excitations of the plasma are described by the classical plasma dielectric function. In the presence of the laser field, the general expressions for the induced potential in the target and the interaction force among the ions within the cluster are derived. Based on the numerical solution of the equations of motion for the constituent ions, the Coulomb explosion patterns and the cluster's stopping power are discussed for a range of laser parameters. Numerical results show that the laser field affects the correlation between the ions and contributes to weaken the wake effect and the stopping power as compared to the laser-free case. On the other hand, the stopping power ratio of H 3 + cluster is higher than the situation of dicluster of H 2 + due to the vicinage effect in the cluster.

  1. The prediction and prevention of voltage collapse in the Finnish power system

    Energy Technology Data Exchange (ETDEWEB)

    Bastman, J.; Lakervi, E. [Tampere Univ. of Tech. (Finland); Hirvonen, R.; Kuronen, P.; Hagman, E. [IVO Group (Finland)

    1994-12-31

    The Finnish power system is a part of the Nordic power system (NORDEL), which includes Finland, Sweden, Norway and the eastern part of Denmark. In NORDEL the transmission distances are long, which implies that the power transmission capacities are determined by stability criteria . The methods to prevent and predict the voltage collapse during severe disturbances are studied using advances simulation program. Results are presented. (author) 10 figs., 1 tab.

  2. Using reference trajectories to predicted uncertain systems: exemplified on a power plant

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Mataji, B.

    2007-01-01

    error depending on the operation point given by references and disturbances. These uncertainty models are stored in a model bank, linear interpolation is applied to the elements of the model bank in order to predict uncertainty bounds on the predictions using the statistics of the past prediction...... uncertainties. It is as well proposed to update the uncertainty prediction models on-line. The potential of the method is illustrated by an example from a coal-fired power plant. This example shows prediction of the uncertainties as a bounded region in which the given system variable can be assumed...

  3. Emotion dysregulation and social competence: stability, change and predictive power.

    Science.gov (United States)

    Berkovits, L D; Baker, B L

    2014-08-01

    Social difficulties are closely linked to emotion dysregulation among children with typical development (TD). Children with developmental delays (DD) are at risk for poor social outcomes, but the relationship between social and emotional development within this population is not well understood. The current study examines the extent to which emotion dysregulation is related to social problems across middle childhood among children with TD or DD. Children with TD (IQ ≥ 85, n = 113) and children with DD (IQ ≤ 75, n = 61) participated in a longitudinal study. Annual assessments were completed at ages 7, 8 and 9 years. At each assessment, mothers reported on children's emotion dysregulation, and both mothers and teachers reported on children's social difficulties. Children with DD had higher levels of emotion dysregulation and social problems at each age than those with TD. Emotion dysregulation and social problems were significantly positively correlated within both TD and DD groups using mother report of social problems, and within the TD group using teacher report of social problems. Among children with TD, emotion dysregulation consistently predicted change in social problems from one year to the next. However, among children with DD, emotion dysregulation offered no unique prediction value above and beyond current social problems. Results suggested that the influence of emotion regulation abilities on social development may be a less salient pathway for children with DD. These children may have more influences, beyond emotion regulation, on their social behaviour, highlighting the importance of directly targeting social skill deficits among children with DD in order to ameliorate their social difficulties. © 2013 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  4. Active Power Optimal Control of Wind Turbines with Doubly Fed Inductive Generators Based on Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Guo Jiuwang

    2015-01-01

    Full Text Available Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF wind power generation system with doubly fed induction generators (DFIG, traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing the operating principles and dynamic characteristics of doubly fed wind turbines, this paper proposes a new active power optimal control method of doubly fed wind turbines based on predictive control theory. This method uses state space model of wind turbines, based on the prediction of the future state of wind turbines, moves horizon optimization, and meanwhile, gets the control signals of pitch angle and generator torque. Simulation results show that the proposed control strategies can guarantee the utilization efficiency for wind energy. Simultaneously, they can improve operation stability of wind turbines and the quality of electric energy.

  5. Offset-Free Direct Power Control of DFIG Under Continuous-Time Model Predictive Control

    DEFF Research Database (Denmark)

    Errouissi, Rachid; Al-Durra, Ahmed; Muyeen, S.M.

    2017-01-01

    This paper presents a robust continuous-time model predictive direct power control for doubly fed induction generator (DFIG). The proposed approach uses Taylor series expansion to predict the stator current in the synchronous reference frame over a finite time horizon. The predicted stator current...... without encompassing the parameters of the machine itself. Hence, no extra power control loop is required in the control structure to ensure smooth operation of the DFIG. The feasibility of the proposed strategy is verified by the experimental results of the grid-connected DFIG and satisfactory...

  6. A new wind power prediction method based on chaotic theory and Bernstein Neural Network

    International Nuclear Information System (INIS)

    Wang, Cong; Zhang, Hongli; Fan, Wenhui; Fan, Xiaochao

    2016-01-01

    The accuracy of wind power prediction is important for assessing the security and economy of the system operation when wind power connects to the grids. However, multiple factors cause a long delay and large errors in wind power prediction. Hence, efficient wind power forecasting approaches are still required for practical applications. In this paper, a new wind power forecasting method based on Chaos Theory and Bernstein Neural Network (BNN) is proposed. Firstly, the largest Lyapunov exponent as a judgment for wind power system's chaotic behavior is made. Secondly, Phase Space Reconstruction (PSR) is used to reconstruct the wind power series' phase space. Thirdly, the prediction model is constructed using the Bernstein polynomial and neural network. Finally, the weights and thresholds of the model are optimized by Primal Dual State Transition Algorithm (PDSTA). The practical hourly data of wind power generation in Xinjiang is used to test this forecaster. The proposed forecaster is compared with several current prominent research findings. Analytical results indicate that the forecasting error of PDSTA + BNN is 3.893% for 24 look-ahead hours, and has lower errors obtained compared with the other forecast methods discussed in this paper. The results of all cases studying confirm the validity of the new forecast method. - Highlights: • Lyapunov exponent is used to verify chaotic behavior of wind power series. • Phase Space Reconstruction is used to reconstruct chaotic wind power series. • A new Bernstein Neural Network to predict wind power series is proposed. • Primal dual state transition algorithm is chosen as the training strategy of BNN.

  7. ESB-based Sensor Web integration for the prediction of electric power supply system vulnerability.

    Science.gov (United States)

    Stoimenov, Leonid; Bogdanovic, Milos; Bogdanovic-Dinic, Sanja

    2013-08-15

    Electric power supply companies increasingly rely on enterprise IT systems to provide them with a comprehensive view of the state of the distribution network. Within a utility-wide network, enterprise IT systems collect data from various metering devices. Such data can be effectively used for the prediction of power supply network vulnerability. The purpose of this paper is to present the Enterprise Service Bus (ESB)-based Sensor Web integration solution that we have developed with the purpose of enabling prediction of power supply network vulnerability, in terms of a prediction of defect probability for a particular network element. We will give an example of its usage and demonstrate our vulnerability prediction model on data collected from two different power supply companies. The proposed solution is an extension of the GinisSense Sensor Web-based architecture for collecting, processing, analyzing, decision making and alerting based on the data received from heterogeneous data sources. In this case, GinisSense has been upgraded to be capable of operating in an ESB environment and combine Sensor Web and GIS technologies to enable prediction of electric power supply system vulnerability. Aside from electrical values, the proposed solution gathers ambient values from additional sensors installed in the existing power supply network infrastructure. GinisSense aggregates gathered data according to an adapted Omnibus data fusion model and applies decision-making logic on the aggregated data. Detected vulnerabilities are visualized to end-users through means of a specialized Web GIS application.

  8. Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

    Energy Technology Data Exchange (ETDEWEB)

    Abbas, Nikhar [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Tom, Nathan M [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-06-03

    Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

  9. A variable capacitance based modeling and power capability predicting method for ultracapacitor

    Science.gov (United States)

    Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

    2018-01-01

    Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

  10. Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Abbas, Nikhar; Tom, Nathan

    2017-09-01

    Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalman filter and autoregressive model to evaluate model predictive control performance.

  11. Smoothing of wind farm output power using prediction based flywheel energy storage system

    Science.gov (United States)

    Islam, Farzana

    Being socially beneficial, economically competitive and environment friendly, wind energy is now considered to be the world's fastest growing renewable energy source. However, the stochastic nature of wind imposes a considerable challenge in the optimal management and operation of wind power system. Wind speed prediction is critical for wind energy conversion system since it greatly influences the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. This thesis focuses on integration of energy storage system with wind farm, considering wind speed prediction in the control scheme to overcome the problems associated with wind power fluctuations. In this thesis, flywheel energy storage system (FESS) with adjustable speed rotary machine has been considered for smoothing of output power in a wind farm composed of a fixed speed wind turbine generator (FSWTG). Since FESS has both active and reactive power compensation ability, it enhances the stability of the system effectively. An efficient energy management system combined with supervisory control unit (SCU) for FESS and wind speed prediction has been developed to improve the smoothing of the wind farm output effectively. Wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction scheme including data error tolerance and ease in adaptability. The model for prediction with ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions.

  12. THE PREDICTIVE POWER OF RELIGIOUS ORIENTATION TYPES ON AMBIVALENT SEXISM

    Directory of Open Access Journals (Sweden)

    Gul Ulubayram

    2016-07-01

    Full Text Available Objective: The purpose of this study is to examine stress and stress related factors in tuberculosis patients. In addition, to determine the impact of socio-demographic variables such as age, gender and educational level over stress symptoms hereby comprises a further objective of this study. Method: The study included totally 129 tuberculosis patients and 161 non-patients (normal group participants. Tuberculosis patients registered in Ankara Tuberculosis Dispensary No.4, and Atatürk Chest Diseases and Chest Surgery Hospital. There are 75 Pulmonary Tuberculosis (AC TB and 54 Extra- Pulmonary Tuberculosis (AD TB patients. As regards data collection tools; Demographic Information Form, Brief Symptom Inventory, Stress Symptoms Scale, Stress Vulnerability Scale, and Stress Coping Scale were used. Results: Within the context of diagnosis groups; it was found that; stress symptoms of tuberculosis patients are higher than the normal group, they use their ineffective coping ways more and their life satisfactions are lower. There exists no gender and diagnosis group main effect in terms of the psychological symptoms of stress, however “gender x diagnosis group” interaction effect draws attention herein. In tuberculosis patients, ineffective coping the stress and relation pleasure variables are confronted as joint variables which are predicting both the psychological and physical health. Another point which draws attention in regression analyzes is that; “education” variable takes place among the variables which predict the psychological symptoms of stress in tuberculosis patients. Conclusion: Under the light of these findings, tuberculosis patients, during their treatment processes, may be encouraged to attend various training programs prepared for stress management and effective dealing strategies with stress. By increasing the patients’ motivation towards the treatment, these programs may provide supplementary benefits to the treatment by

  13. Nuclear Fission: from more phenomenology and adjusted parameters to more fundamental theory and increased predictive power

    Science.gov (United States)

    Bulgac, Aurel; Jin, Shi; Magierski, Piotr; Roche, Kenneth; Schunck, Nicolas; Stetcu, Ionel

    2017-11-01

    Two major recent developments in theory and computational resources created the favorable conditions for achieving a microscopic description of fission dynamics in classically allowed regions of the collective potential energy surface, almost eighty years after its discovery in 1939 by Hahn and Strassmann [1]. The first major development was in theory, the extension of the Time-Dependent Density Functional Theory (TDDFT) [2-5] to superfluid fermion systems [6]. The second development was in computing, the emergence of powerful enough supercomputers capable of solving the complex systems of equations describing the time evolution in three dimensions without any restrictions of hundreds of strongly interacting nucleons. Thus the conditions have been created to renounce phenomenological models and incomplete microscopic treatments with uncontrollable approximations and/or assumptions in the description of the complex dynamics of fission. Even though the available nuclear energy density functionals (NEDFs) are phenomenological still, their accuracy is improving steadily and the prospects of being able to perform calculations of the nuclear fission dynamics and to predict many properties of the fission fragments, otherwise not possible to extract from experiments.

  14. Lifetime prediction of high-power press-pack IGBTs in wind power applications

    DEFF Research Database (Denmark)

    Busca, Cristian

    if the chip characteristics have been properly matched. In this PhD project the effect of mechanical clamping conditions on the chip-level thermal cycling and chip-level lifetime of PP IGBTs in wind power applications is investigated. This is achieved through co-simulation of a number of different models...... and decoupling of the generator and grid sides. Press-Pack (PP) Insulated Gate Bipolar Transistors (IGBTs) are promising semiconductor devices for the next generation large WTs due to the advantages they offer in terms of power capability, power density and thermal cycling capability. PP IGBTs require proper...... mechanical clamping in order to ensure even clamping force distribution among the chips. Since clamping force is linked to both thermal contact resistance and electrical contact resistance, even clamping force distribution among the chips ensures even loading of the chips and maximized reliability...

  15. Using data-driven approach for wind power prediction: A comparative study

    International Nuclear Information System (INIS)

    Taslimi Renani, Ehsan; Elias, Mohamad Fathi Mohamad; Rahim, Nasrudin Abd.

    2016-01-01

    Highlights: • Double exponential smoothing is the most accurate model in wind speed prediction. • A two-stage feature selection method is proposed to select most important inputs. • Direct prediction illustrates better accuracy than indirect prediction. • Adaptive neuro fuzzy inference system outperforms data mining algorithms. • Random forest performs the worst compared to other data mining algorithm. - Abstract: Although wind energy is intermittent and stochastic in nature, it is increasingly important in the power generation due to its sustainability and pollution-free. Increased utilization of wind energy sources calls for more robust and efficient prediction models to mitigate uncertainties associated with wind power. This research compares two different approaches in wind power forecasting which are indirect and direct prediction methods. In indirect method, several times series are applied to forecast the wind speed, whereas the logistic function with five parameters is then used to forecast the wind power. In this study, backtracking search algorithm with novel crossover and mutation operators is employed to find the best parameters of five-parameter logistic function. A new feature selection technique, combining the mutual information and neural network is proposed in this paper to extract the most informative features with a maximum relevancy and minimum redundancy. From the comparative study, the results demonstrate that, in the direct prediction approach where the historical weather data are used to predict the wind power generation directly, adaptive neuro fuzzy inference system outperforms five data mining algorithms namely, random forest, M5Rules, k-nearest neighbor, support vector machine and multilayer perceptron. Moreover, it is also found that the mean absolute percentage error of the direct prediction method using adaptive neuro fuzzy inference system is 1.47% which is approximately less than half of the error obtained with the

  16. Chronic health conditions and depressive symptoms strongly predict persistent food insecurity among rural low-income families.

    Science.gov (United States)

    Hanson, Karla L; Olson, Christine M

    2012-08-01

    Longitudinal studies of food insecurity have not considered the unique circumstances of rural families. This study identified factors predictive of discontinuous and persistent food insecurity over three years among low-income families with children in rural counties in 13 U.S. states. Respondents reported substantial knowledge of community resources, food and finance skills, and use of formal public food assistance, yet 24% had persistent food insecurity, and another 41% were food insecure for one or two years. Multivariate multinomial regression models tested relationships between human capital, social support, financial resources, expenses, and food insecurity. Enduring chronic health conditions increased the risk of both discontinuous and persistent food insecurity. Lasting risk for depression predicted only persistent food insecurity. Education beyond high school was the only factor found protective against persistent food insecurity. Access to quality physical and mental health care services are essential to ameliorate persistent food insecurity among rural, low-income families.

  17. Self-Conscious Shyness: Growth during Toddlerhood, Strong Role of Genetics, and No Prediction from Fearful Shyness

    OpenAIRE

    Eggum-Wilkens, Natalie D.; Lemery-Chalfant, Kathryn; Aksan, Nazan; Goldsmith, H. Hill

    2014-01-01

    Fearful and self-conscious subtypes of shyness have received little attention in the empirical literature. Study aims included: 1) determining if fearful shyness predicted self-conscious shyness, 2) describing development of self-conscious shyness, and 3) examining genetic and environmental contributions to fearful and self-conscious shyness. Observed self-conscious shyness was examined at 19, 22, 25, and 28 months in same-sex twins (MZ = 102, DZ = 111, missing zygosity = 3 pairs). Self-consc...

  18. Wind Power Prediction Based on LS-SVM Model with Error Correction

    Directory of Open Access Journals (Sweden)

    ZHANG, Y.

    2017-02-01

    Full Text Available As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM. Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.

  19. PREDICTION OF POWER GENERATION OF SMALL SCALE VERTICAL AXIS WIND TURBINE USING FUZZY LOGIC

    Directory of Open Access Journals (Sweden)

    Altab Hossain

    2009-01-01

    Full Text Available Renewable energy from the wind turbine has been focused for the alternative source of power generation due to the following advances of the of the wind turbine. Firstly, the wind turbine is highly efficient and eco-friendly. Secondly, the turbine has the ability to response for the changeable power generation based on the wind velocity and structural framework. However, the competitive efficiency of the wind turbine is necessary to successfully alternate the conventional power sources. The most relevant factor which affects the overall efficiency of the wind turbine is the wind velocity and the relative turbine dimensions. Artificial intelligence systems are widely used technology that can learn from examples and are able to deal with non-linear problems. Compared with traditional approach, fuzzy logic approach is more efficient for the representation, manipulation and utilization. Therefore, the primary purpose of this work was to investigate the relationship between wind turbine power generation and wind velocity, and to illustrate how fuzzy expert system might play an important role in prediction of wind turbine power generation. The main purpose of the measurement over the small scaled prototype vertical axis wind turbine for the wind velocity is to predict the performance of full scaled H-type vertical axis wind turbine. Prediction of power generation at the different wind velocities has been tested at the Thermal Laboratory of Faculty of Engineering, Universiti Industri Selangor (UNISEL and results concerning the daily prediction have been obtained.

  20. State-Space Model Predictive Control Method for Core Power Control in Pressurized Water Reactor Nuclear Power Stations

    Directory of Open Access Journals (Sweden)

    Guoxu Wang

    2017-02-01

    Full Text Available A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP. The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

  1. State-space model predictive control method for core power control in pressurized water reactor nuclear power stations

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Guo Xu; Wu, Jie; Zeng, Bifan; Wu, Wangqiang; Ma, Xiao Qian [School of Electric Power, South China University of Technology, Guangzhou (China); Xu, Zhibin [Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou (China)

    2017-02-15

    A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

  2. Wind Power predictability a risk factor in the design, construction and operation of Wind Generation Turbines

    Science.gov (United States)

    Thiesen, J.; Gulstad, L.; Ristic, I.; Maric, T.

    2010-09-01

    Summit: The wind power predictability is often a forgotten decision and planning factor for most major wind parks, both onshore and offshore. The results of the predictability are presented after having examined a number of European offshore and offshore parks power predictability by using three(3) mesoscale model IRIE_GFS and IRIE_EC and WRF. Full description: It is well known that the potential wind production is changing with latitude and complexity in terrain, but how big are the changes in the predictability and the economic impacts on a project? The concept of meteorological predictability has hitherto to some degree been neglected as a risk factor in the design, construction and operation of wind power plants. Wind power plants are generally built in places where the wind resources are high, but these are often also sites where the predictability of the wind and other weather parameters is comparatively low. This presentation addresses the question of whether higher predictability can outweigh lower average wind speeds with regard to the overall economy of a wind power project. Low predictability also tends to reduce the value of the energy produced. If it is difficult to forecast the wind on a site, it will also be difficult to predict the power production. This, in turn, leads to increased balance costs and a less reduced carbon emission from the renewable source. By investigating the output from three(3) mesoscale models IRIE and WRF, using ECMWF and GFS as boundary data over a forecasting period of 3 months for 25 offshore and onshore wind parks in Europe, the predictability are mapped. Three operational mesoscale models with two different boundary data have been chosen in order to eliminate the uncertainty with one mesoscale model. All mesoscale models are running in a 10 km horizontal resolution. The model output are converted into "day a head" wind turbine generation forecasts by using a well proven advanced physical wind power model. The power models

  3. Off-Ice Anaerobic Power Does Not Predict On-Ice Repeated Shift Performance in Hockey.

    Science.gov (United States)

    Peterson, Ben J; Fitzgerald, John S; Dietz, Calvin C; Ziegler, Kevin S; Baker, Sarah E; Snyder, Eric M

    2016-09-01

    Peterson, BJ, Fitzgerald, JS, Dietz, CC, Ziegler, KS, Baker, SE, and Snyder, EM. Off-ice anaerobic power does not predict on-ice repeated shift performance in hockey. J Strength Cond Res 30(9): 2375-2381, 2016-Anaerobic power is a significant predictor of acceleration and top speed in team sport athletes. Historically, these findings have been applied to ice hockey although recent research has brought their validity for this sport into question. As ice hockey emphasizes the ability to repeatedly produce power, single bout anaerobic power tests should be examined to determine their ability to predict on-ice performance. We tested whether conventional off-ice anaerobic power tests could predict on-ice acceleration, top speed, and repeated shift performance. Forty-five hockey players, aged 18-24 years, completed anthropometric, off-ice, and on-ice tests. Anthropometric and off-ice testing included height, weight, body composition, vertical jump, and Wingate tests. On-ice testing consisted of acceleration, top speed, and repeated shift fatigue tests. Vertical jump (VJ) (r = -0.42; r = -0.58), Wingate relative peak power (WRPP) (r = -0.32; r = -0.43), and relative mean power (WRMP) (r = -0.34; r = -0.48) were significantly correlated (p ≤ 0.05) to on-ice acceleration and top speed, respectively. Conversely, none of the off-ice tests correlated with on-ice repeated shift performance, as measured by first gate, second gate, or total course fatigue; VJ (r = 0.06; r = 0.13; r = 0.09), WRPP (r = 0.06; r = 0.14; r = 0.10), or WRMP (r = -0.10; r = -0.01; r = -0.01). Although conventional off-ice anaerobic power tests predict single bout on-ice acceleration and top speed, they neither predict the repeated shift ability of the player, nor are good markers for performance in ice hockey.

  4. K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering

    Directory of Open Access Journals (Sweden)

    Lv Tao

    2017-01-01

    Full Text Available Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1 the predictive power of a pattern varies a great deal for different shapes and (2 each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.

  5. Does Spontaneous Favorability to Power (vs. Universalism) Values Predict Spontaneous Prejudice and Discrimination?

    Science.gov (United States)

    Souchon, Nicolas; Maio, Gregory R; Hanel, Paul H P; Bardin, Brigitte

    2017-10-01

    We conducted five studies testing whether an implicit measure of favorability toward power over universalism values predicts spontaneous prejudice and discrimination. Studies 1 (N = 192) and 2 (N = 86) examined correlations between spontaneous favorability toward power (vs. universalism) values, achievement (vs. benevolence) values, and a spontaneous measure of prejudice toward ethnic minorities. Study 3 (N = 159) tested whether conditioning participants to associate power values with positive adjectives and universalism values with negative adjectives (or inversely) affects spontaneous prejudice. Study 4 (N = 95) tested whether decision bias toward female handball players could be predicted by spontaneous attitude toward power (vs. universalism) values. Study 5 (N = 123) examined correlations between spontaneous attitude toward power (vs. universalism) values, spontaneous importance toward power (vs. universalism) values, and spontaneous prejudice toward Black African people. Spontaneous positivity toward power (vs. universalism) values was associated with spontaneous negativity toward minorities and predicted gender bias in a decision task, whereas the explicit measures did not. These results indicate that the implicit assessment of evaluative responses attached to human values helps to model value-attitude-behavior relations. © 2016 The Authors. Journal of Personality Published by Wiley Periodicals, Inc.

  6. Wide-area Power System Oscillation Damping using Model Predictive Control Technique

    Science.gov (United States)

    Mohamed, Tarek Hassan; Abdel-Rahim, Abdel-Moamen Mohammed; Hassan, Ahmed Abd-Eltawwab; Hiyama, Takashi

    This paper presents a new approach to deal with the problem of robust tuning of power system stabilizer (PSS) and automatic voltage regulator (AVR) in multi-machine power systems. The proposed method is based on a model predictive control (MPC) technique, for improvement stability of the wide-area power system with multiple generators and distribution systems including dispersed generations. The proposed method provides better damping of power system oscillations under small and large disturbances even with the inclusion of local PSSs. The effectiveness of the proposed approach is demonstrated through a two areas, four machines power system. A performance comparison between the proposed controller and some of other controllers is carried out confirming the superiority of the proposed technique. It has also been observed that the proposed algorithm can be successfully applied to larger multiarea power systems and do not suffer with computational difficulties. The proposed algorithm carried out using MATLAB/SIMULINK software package.

  7. Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

    Science.gov (United States)

    Dedes, I.; Dudek, J.

    2018-03-01

    We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods–Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods–Saxon form and the more advanced version with the self-consistent density-dependent spin–orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions (‘predictive power’) under realistic parameter adjustment conditions.

  8. Analysis of the vibrations of the low power ORC turbines operating under conditions of strongly developed hydrodynamic instability

    Science.gov (United States)

    Kiciński, Jan

    2018-01-01

    The paper presents the results of the analysis of the dynamic performance of the rotor being a component of the ORC turbine set with the net electrical output of 100 kW and the nominal speed of 9000 rpm. The research was conducted using tools capable of performing the necessary simulation of the system operating under highly unstable conditions, i.e., in a strongly nonlinear regime. In this regard, the author of the paper followed the subsequent phases of whirl/whip formation manifested in the fluid film. Constructional solutions within the scope of the bearing were examined with non-conventional lubricating mediums (low boiling mediums). On the basis of those scientific studies, the decision to build a working prototype of the machine was taken. Such a prototype has already been manufactured, having regard to the outcome of the conducted analyses. The research presented herein produced interesting results showing that, under the conditions of hydrodynamic instability, the phenomena taking place inside the lubricating gap of the slide bearing are not recurrent for each individual rotor revolution, notwithstanding the fact that the external excitation forces acting on the system are fully repeatable. The research tools were presented that allow a detailed qualitative and quantitative description of such phenomena.

  9. Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm

    Directory of Open Access Journals (Sweden)

    Qunli Wu

    2015-12-01

    Full Text Available Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA–least squares support vector machine (LSSVM hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.

  10. Hierarchical model-based predictive control of a power plant portfolio

    DEFF Research Database (Denmark)

    Edlund, Kristian; Bendtsen, Jan Dimon; Jørgensen, John Bagterp

    2011-01-01

    optimisation problem, which is solved using Dantzig–Wolfe decomposition. This decomposition yields improved computational efficiency and better scalability compared to centralised methods.The proposed control scheme is compared to an existing, state-of-the-art portfolio control system (operated by DONG Energy...... control” – becomes increasingly important as the ratio of renewable energy in a power system grows. As a consequence, tomorrow's “smart grids” require highly flexible and scalable control systems compared to conventional power systems. This paper proposes a hierarchical model-based predictive control......One of the main difficulties in large-scale implementation of renewable energy in existing power systems is that the production from renewable sources is difficult to predict and control. For this reason, fast and efficient control of controllable power producing units – so-called “portfolio...

  11. A Model for Prediction of Propulsion Power and Emissions – Tankers and Bulk Carriers

    DEFF Research Database (Denmark)

    Lützen, Marie; Kristensen, Hans Otto Holmegaard

    To get an idea of the reduction in propulsion power and associated emissions by varying the speed and other ship design main parameters, a generic model for parameter studies of tankers and bulk carriers has been developed. With only a few input parameters of which the maximum deadweight capacity...... is the primary input a proposal for the main dimensions is made. Based on these dimensions and other ship particulars which are determined by the program the necessary installed propulsion power can be calculated. By adjusting the vessel design, i.e. the suggested main dimensions, and varying the speed...... it is possible to estimate the influence of the different parameters on the power demand. The model is based on previously well-established power prediction methods which have been updated and verified by model test results and full-scale data, meaning that the predictions are up to date according to modern ship...

  12. Modeling and Model Predictive Power and Rate Control of Wireless Communication Networks

    Directory of Open Access Journals (Sweden)

    Cunwu Han

    2014-01-01

    Full Text Available A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI techniques. Simulation results are given to illustrate the effectiveness of the proposed method.

  13. A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction

    International Nuclear Information System (INIS)

    Yesilbudak, Mehmet; Sagiroglu, Seref; Colak, Ilhami

    2017-01-01

    Highlights: • An accurate wind power prediction model is proposed for very short-term horizon. • The k-nearest neighbor classifier is implemented based on the multi-tupled inputs. • The variation of wind power prediction errors is evaluated in various aspects. • Our approach shows the superior prediction performance over the persistence method. - Abstract: With the growing share of wind power production in the electric power grids, many critical challenges to the grid operators have been emerged in terms of the power balance, power quality, voltage support, frequency stability, load scheduling, unit commitment and spinning reserve calculations. To overcome such problems, numerous studies have been conducted to predict the wind power production, but a small number of them have attempted to improve the prediction accuracy by employing the multidimensional meteorological input data. The novelties of this study lie in the proposal of an efficient and easy to implement very short-term wind power prediction model based on the k-nearest neighbor classifier (kNN), in the usage of wind speed, wind direction, barometric pressure and air temperature parameters as the multi-tupled meteorological inputs and in the comparison of wind power prediction results with respect to the persistence reference model. As a result of the achieved patterns, we characterize the variation of wind power prediction errors according to the input tuples, distance measures and neighbor numbers, and uncover the most influential and the most ineffective meteorological parameters on the optimization of wind power prediction results.

  14. Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

    Science.gov (United States)

    Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

    2011-12-01

    This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

  15. Application of Model Predictive Control for Active Load Management in a Distributed Power System with High Wind Penetration

    DEFF Research Database (Denmark)

    Zong, Yi; Kullmann, Daniel; Thavlov, Anders

    2012-01-01

    management. It also presents in detail how to implement a thermal model predictive controller (MPC) for the heaters' power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching...

  16. Lower Serum Zinc and Higher CRP Strongly Predict Prenatal Depression and Physio-somatic Symptoms, Which All Together Predict Postnatal Depressive Symptoms.

    Science.gov (United States)

    Roomruangwong, Chutima; Kanchanatawan, Buranee; Sirivichayakul, Sunee; Mahieu, Boris; Nowak, Gabriel; Maes, Michael

    2017-03-01

    Pregnancy and delivery are associated with activation of immune-inflammatory pathways which may prime parturients to develop postnatal depression. There are, however, few data on the associations between immune-inflammatory pathways and prenatal depression and physio-somatic symptoms. This study examined the associations between serum zinc, C-reactive protein (CRP), and haptoglobin at the end of term and prenatal physio-somatic symptoms (fatigue, back pain, muscle pain, dyspepsia, obstipation) and prenatal and postnatal depressive and anxiety symptoms as measured using the Edinburgh Postnatal Depression Scale (EPDS), Beck Depression Inventory (BDI), Hamilton Depression Rating Scale (HAMD), and Spielberger's State Anxiety Inventory (STAI). Zinc and haptoglobin were significantly lower and CRP increased at the end of term as compared with non-pregnant women. Prenatal depression was predicted by lower zinc and lifetime history of depression, anxiety, and premenstrual tension syndrome (PMS). The latter histories were also significantly and inversely related to lower zinc. The severity of prenatal EDPS, HAMD, BDI, STAI, and physio-somatic symptoms was predicted by fatigue in the first and second trimesters, a positive life history of depression, anxiety, and PMS, and lower zinc and higher CRP. Postnatal depressive symptoms are predicted by prenatal depression, physio-somatic symptoms, zinc and CRP. Prenatal depressive and physio-somatic symptoms have an immune-inflammatory pathophysiology, while postnatal depressive symptoms are highly predicted by prenatal immune activation, prenatal depression, and a lifetime history of depression and PMS. Previous episodes of depression, anxiety disorders, and PMS may prime pregnant females to develop prenatal and postnatal depressive symptoms via activated immune pathways.

  17. Interpreting the Strongly Lensed Supernova iPTF16geu: Time Delay Predictions, Microlensing, and Lensing Rates

    Energy Technology Data Exchange (ETDEWEB)

    More, Anupreeta; Oguri, Masamune; More, Surhud [Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), University of Tokyo, Chiba 277-8583 (Japan); Suyu, Sherry H. [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, D-85748 Garching (Germany); Lee, Chien-Hsiu, E-mail: anupreeta.more@ipmu.jp [Subaru Telescope, National Astronomical Observatory of Japan, 650 North Aohoku Place, Hilo, HI 96720 (United States)

    2017-02-01

    We present predictions for time delays between multiple images of the gravitationally lensed supernova, iPTF16geu, which was recently discovered from the intermediate Palomar Transient Factory (iPTF). As the supernova is of Type Ia where the intrinsic luminosity is usually well known, accurately measured time delays of the multiple images could provide tight constraints on the Hubble constant. According to our lens mass models constrained by the Hubble Space Telescope F814W image, we expect the maximum relative time delay to be less than a day, which is consistent with the maximum of 100 hr reported by Goobar et al. but places a stringent upper limit. Furthermore, the fluxes of most of the supernova images depart from expected values suggesting that they are affected by microlensing. The microlensing timescales are small enough that they may pose significant problems to measure the time delays reliably. Our lensing rate calculation indicates that the occurrence of a lensed SN in iPTF is likely. However, the observed total magnification of iPTF16geu is larger than expected, given its redshift. This may be a further indication of ongoing microlensing in this system.

  18. Model predictive control for power fluctuation supression in hybrid wind/PV/battery systems

    DEFF Research Database (Denmark)

    You, Shi; Liu, Zongyu; Zong, Yi

    2015-01-01

    A hybrid energy system, the combination of wind turbines, PV panels and battery storage with effective control mechanism, represents a promising solution to the power fluctuation problem when integrating renewable energy resources (RES) into conventional power systems. This paper proposes a model...... predictive control (MPC)-based algorithm for battery management in a hybrid wind/PV/battery system to suppress the short-term power fluctuation on the ‘minute’ scale. A case study with data collected from a practical hybrid system setup is used to demonstrate the effectiveness of the proposed algorithm...

  19. Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant

    Directory of Open Access Journals (Sweden)

    Xiangjie Liu

    2014-01-01

    Full Text Available Reliable power and temperature control in pressurized water reactor (PWR nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC, by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation.

  20. Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui

    2017-01-01

    This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...

  1. Nonlocal response functions for predicting shear flow of strongly inhomogeneous fluids. II. Sinusoidally driven shear and multisinusoidal inhomogeneity.

    Science.gov (United States)

    Dalton, Benjamin A; Glavatskiy, Kirill S; Daivis, Peter J; Todd, B D

    2015-07-01

    We use molecular-dynamics computer simulations to investigate the density, strain-rate, and shear-pressure responses of a simple model atomic fluid to transverse and longitudinal external forces. We have previously introduced a response function formalism for describing the density, strain-rate, and shear-pressure profiles in an atomic fluid when it is perturbed by a combination of longitudinal and transverse external forces that are independent of time and have a simple sinusoidal spatial variation. In this paper, we extend the application of the previously introduced formalism to consider the case of a longitudinal force composed of multiple sinusoidal components in combination with a single-component sinusoidal transverse force. We find that additional harmonics are excited in the density, strain-rate, and shear-pressure profiles due to couplings between the force components. By analyzing the density, strain-rate, and shear-pressure profiles in Fourier space, we are able to evaluate the Fourier coefficients of the response functions, which now have additional components describing the coupling relationships. Having evaluated the Fourier coefficients of the response functions, we are then able to accurately predict the density, velocity, and shear-pressure profiles for fluids that are under the influence of a longitudinal force composed of two or three sinusoidal components combined with a single-component sinusoidal transverse force. We also find that in the case of a multisinusoidal longitudinal force, it is sufficient to include only pairwise couplings between different longitudinal force components. This means that it is unnecessary to include couplings between three or more force components in the case of a longitudinal force composed of many Fourier components, and this paves the way for a highly accurate but tractable treatment of nonlocal transport phenomena in fluids with density and strain-rate inhomogeneities on the molecular length scale.

  2. Assess and Predict Automatic Generation Control Performances for Thermal Power Generation Units Based on Modeling Techniques

    Science.gov (United States)

    Zhao, Yan; Yang, Zijiang; Gao, Song; Liu, Jinbiao

    2018-02-01

    Automatic generation control(AGC) is a key technology to maintain real time power generation and load balance, and to ensure the quality of power supply. Power grids require each power generation unit to have a satisfactory AGC performance, being specified in two detailed rules. The two rules provide a set of indices to measure the AGC performance of power generation unit. However, the commonly-used method to calculate these indices is based on particular data samples from AGC responses and will lead to incorrect results in practice. This paper proposes a new method to estimate the AGC performance indices via system identification techniques. In addition, a nonlinear regression model between performance indices and load command is built in order to predict the AGC performance indices. The effectiveness of the proposed method is validated through industrial case studies.

  3. Predicting Optimal Resolving Power for Ambient Pressure Ion Mobility Spectrometry (IMS)

    Science.gov (United States)

    Kanu, Abu B.; Gribb, Molly M.; Hill, Herbert H

    2010-01-01

    Although diffusion theory predicts that IMS resolving power increases with the square root of the voltage applied across the drift tube, in practice there exists an optimum voltage above which resolving power decreases. This optimum voltage was determined to be both compound and initial ion pulse width-dependent. A “conditional” resolving power equation is introduced that can be used to quickly approximate realistic resolving powers for specific instrumental operating parameters and compounds. Using four common environmental contaminants [trichloroethylene (TCE), tetrachloroethylene (PCE), methyl tert-butyl ether (MTBE) and methyl iso-butyl ketone (MIBK)], diffusion-limited (theoretical), Rd, conditional, Rc, and actual (or measured), Rm, IMS resolving powers were determined and compared for a small IMS instrument designed for subsurface measurements. Detection limits determined at the optimal resolving power for the environmental contaminants ranged from 18 parts per trillion volume-to-volume (pptv) to 80 parts per billion volume-to-volume (ppbv). The maximal measured resolving power for our small, ambient-pressure stand-alone IMS ranged from 42 to 54, yielding an IMS resolving power efficiency, defined as Rm/Rc × 100%, of 56 to 74% of the maximal conditional resolving power possible. PMID:18683951

  4. Rehabilitation after stroke: predictive power of Barthel Index versus a cognitive and a motor index

    DEFF Research Database (Denmark)

    Engberg, A; Bentzen, L; Garde, B

    1995-01-01

    The aim of the present study was to investigate the predictive power of ratings of Barthel Index at Day 40 post stroke, compared with and/or combined with simultaneous ratings from a mobility scale (EG motor index) and a rather simple cognitive test scale (CT50). The parameter to be individually...... predicted was the need for special living facilities and support at discharge from a rehabilitation hospital, as well as six months later; 53 stroke patients with age median 68 years were included in this prospective study. It was shown that a combination of Barthel Index and CT50 had a stronger predictive...

  5. Aggression in Primary Schools: The Predictive Power of the School and Home Environment

    Science.gov (United States)

    Kozina, Ana

    2015-01-01

    In this study, we analyse the predictive power of home and school environment-related factors for determining pupils' aggression. The multiple regression analyses are performed for fourth- and eighth-grade pupils based on the Trends in Mathematics and Science Study (TIMSS) 2007 (N = 8394) and TIMSS 2011 (N = 9415) databases for Slovenia. At the…

  6. Loneliness among University Students: Predictive Power of Sex Roles and Attachment Styles on Loneliness

    Science.gov (United States)

    Ilhan, Tahsin

    2012-01-01

    This study examined the predictive power of sex roles and attachment styles on loneliness. A total of 188 undergraduate students (114 female, and 74 male) from Gazi University completed the Bem Sex Role Inventory, UCLA Loneliness Scale, and Relationship Scales Questionnaire. Hierarchic Multiple Regression analysis and t-test were used to test…

  7. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Pedersen, Benjamin Pjedsted; Larsen, Jan

    2009-01-01

    Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction o...

  8. Identification of the Predictive Power of Five Factor Personality Traits for Individual Instrument Performance Anxiety

    Science.gov (United States)

    Özdemir, Gökhan; Dalkiran, Esra

    2017-01-01

    This study, with the aim of identifying the predictive power of the five-factor personality traits of music teacher candidates on individual instrument performance anxiety, was designed according to the relational screening model. The study population was students attending the Music Education branch of Fine Arts Education Departments in…

  9. Research Design and the Predictive Power of Measures of Self-Efficacy

    Science.gov (United States)

    Moriarty, Beverley

    2014-01-01

    The purpose of this enquiry was to examine how research design impacts on the predictive power of measures of self-efficacy. Three cautions for designing research into self-efficacy drawn from the seminal work of Albert Bandura (1986) and a further caution proposed by the current author together form the analytical framework for this enquiry. For…

  10. Rehabilitation after stroke: predictive power of Barthel Index versus a cognitive and a motor index

    DEFF Research Database (Denmark)

    Engberg, A; Bentzen, L; Garde, B

    1995-01-01

    The aim of the present study was to investigate the predictive power of ratings of Barthel Index at Day 40 post stroke, compared with and/or combined with simultaneous ratings from a mobility scale (EG motor index) and a rather simple cognitive test scale (CT50). The parameter to be individually ...

  11. The Prediction Power of Servant and Ethical Leadership Behaviours of Administrators on Teachers' Job Satisfaction

    Science.gov (United States)

    Güngör, Semra Kiranli

    2016-01-01

    The purpose of this study is to identify servant leadership and ethical leadership behaviors of administrators and the prediction power of these behaviors on teachers' job satisfaction according to the views of schoolteachers. This research, figured in accordance with the quantitative research processes. The target population of the research has…

  12. Achievement motivation revisited : New longitudinal data to demonstrate its predictive power

    NARCIS (Netherlands)

    Hustinx, P.W.J.; Kuyper, H.; Van der Werf, M.P.C.; Dijkstra, Pieternel

    2009-01-01

    During recent decades, the classical one-dimensional concept of achievement motivation has become less popular among motivation researchers. This study aims to revive the concept by demonstrating its predictive power using longitudinal data from two cohort samples, each with 20,000 Dutch secondary

  13. Towards space based verification of CO2 emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

    Directory of Open Access Journals (Sweden)

    T. Krings

    2011-12-01

    Full Text Available Carbon dioxide (CO2 is the most important man-made greenhouse gas (GHG that cause global warming. With electricity generation through fossil-fuel power plants now being the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010, random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP. CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO with an 828-km orbit height, local time ascending node (LTAN of 13:30 (01:30 p.m. LT and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat has the potential to verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1 with a systematic error of less than ~4.9% and a random error of less than ~6.7% for 50% of all the power plants. For 90% of all the power plants, the systematic error was less than ~12.4% and the random error was less than ~13%. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other

  14. Predicting punching acceleration from selected strength and power variables in elite karate athletes: a multiple regression analysis.

    Science.gov (United States)

    Loturco, Irineu; Artioli, Guilherme Giannini; Kobal, Ronaldo; Gil, Saulo; Franchini, Emerson

    2014-07-01

    This study investigated the relationship between punching acceleration and selected strength and power variables in 19 professional karate athletes from the Brazilian National Team (9 men and 10 women; age, 23 ± 3 years; height, 1.71 ± 0.09 m; and body mass [BM], 67.34 ± 13.44 kg). Punching acceleration was assessed under 4 different conditions in a randomized order: (a) fixed distance aiming to attain maximum speed (FS), (b) fixed distance aiming to attain maximum impact (FI), (c) self-selected distance aiming to attain maximum speed, and (d) self-selected distance aiming to attain maximum impact. The selected strength and power variables were as follows: maximal dynamic strength in bench press and squat-machine, squat and countermovement jump height, mean propulsive power in bench throw and jump squat, and mean propulsive velocity in jump squat with 40% of BM. Upper- and lower-body power and maximal dynamic strength variables were positively correlated to punch acceleration in all conditions. Multiple regression analysis also revealed predictive variables: relative mean propulsive power in squat jump (W·kg-1), and maximal dynamic strength 1 repetition maximum in both bench press and squat-machine exercises. An impact-oriented instruction and a self-selected distance to start the movement seem to be crucial to reach the highest acceleration during punching execution. This investigation, while demonstrating strong correlations between punching acceleration and strength-power variables, also provides important information for coaches, especially for designing better training strategies to improve punching speed.

  15. Predictability of the power output of three wave energy technologies in the Danish North Sea

    DEFF Research Database (Denmark)

    Fernández-Chozas, J.; Jensen, N.E. Helstrup; Sørensen, H.C.

    2013-01-01

    The paper addresses an important challenge towards the integration of the electricity generated by wave energy converters into the electric grid. Particularly, it looks into the role of wave energy within day-ahead electricity markets. For that the predictability of the theoretical power outputs...... of three wave energy technologies in the Danish North Sea are examined. The simultaneous and co-located forecast and buoy-measured wave parameters at Hanstholm, Denmark, during a non-consecutive autumn and winter 3-month period form the basis of the investigation. The objective of the study is to assess...... show that the errors in day-ahead predictions (in terms of scatter index) of the significant wave height, zero crossing period and wave power are 22%, 11% and 74%, respectively; and of the normalised theoretical power outputs of Pelamis, Wave Dragon and Wavestar are 37%, 39% and 54%, respectively...

  16. Model Predictive Load Frequency Control of two-area Interconnected Time Delay Power System with TCSC

    Science.gov (United States)

    Deng, Yan; Liu, Wenze

    2017-05-01

    In order to reduce the influence of non-linear constraint and time delay on load frequency control of interconnected power system, this paper, based on Model Predictive Control (MPC), designed a load frequency control scheme for two-area interconnected power system with TCSC device. First, considering the Generation Rate Constraint (GRC) and time delay, this paper builds the dynamics model of two-area interconnected power system with Thyristor Controlled Series Compensation device (TCSC). Then the whole system is decomposed into two subsystems. And each subsystem has its own local area MPC controller. Second, collaborative control is implemented by integrating the control information (measurement value, predictive value, etc.) of subsystems’ MPC controllers into the local control goal. In the end, under consideration of physical constraints, the Matlab simulation is conducted. The calculation results showed that the MPC strategy has better dynamic performance and robustness compared to the traditional PI control.

  17. Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

    Directory of Open Access Journals (Sweden)

    Yanjun Zhang

    2015-01-01

    Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

  18. Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

    Science.gov (United States)

    Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

    2017-04-05

    Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

  19. Is It Really Self-Control? Examining the Predictive Power of the Delay of Gratification Task

    Science.gov (United States)

    Duckworth, Angela L.; Tsukayama, Eli; Kirby, Teri A.

    2013-01-01

    This investigation tests whether the predictive power of the delay of gratification task (colloquially known as the “marshmallow test”) derives from its assessment of self-control or of theoretically unrelated traits. Among 56 school-age children in Study 1, delay time was associated with concurrent teacher ratings of self-control and Big Five conscientiousness—but not with other personality traits, intelligence, or reward-related impulses. Likewise, among 966 preschool children in Study 2, delay time was consistently associated with concurrent parent and caregiver ratings of self-control but not with reward-related impulses. While delay time in Study 2 was also related to concurrently measured intelligence, predictive relations with academic, health, and social outcomes in adolescence were more consistently explained by ratings of effortful control. Collectively, these findings suggest that delay task performance may be influenced by extraneous traits, but its predictive power derives primarily from its assessment of self-control. PMID:23813422

  20. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

    Directory of Open Access Journals (Sweden)

    Qiang Ni

    2017-10-01

    Full Text Available High quality photovoltaic (PV power prediction intervals (PIs are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

  1. Prediction of power ramp defects - development of a physically based model and evaluation of existing criteria

    International Nuclear Information System (INIS)

    Notley, M.J.F.; Kohn, E.

    2001-01-01

    Power-ramp induced fuel failure is not a problem in the present CANDU reactors. The current empirical correlations that define probability of failure do not agree one-with-another and do not allow extrapolation outside the database. A new methodology, based on physical processes, is presented and compared to data. The methodology calculates the pre-ramp sheath stress and the incremental stress during the ramp, and whether or not there is a defect is predicted based on a failure threshold stress. The proposed model confirms the deductions made by daSilva from an empirical 'fit' to data from the 1988 PNGS power ramp failure incident. It is recommended that daSilvas' correlation be used as reference for OPG (Ontario Power Generation) power reactor fuel, and that extrapolation be performed using the new model. (author)

  2. Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

    Science.gov (United States)

    Cepowski, Tomasz

    2017-06-01

    The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

  3. Validity of field assessments to predict peak muscle power in preschoolers.

    Science.gov (United States)

    King-Dowling, Sara; Proudfoot, Nicole A; Cairney, John; Timmons, Brian W

    2017-08-01

    Field-based fitness assessments are time- and cost-efficient. However, no studies to date have reported the predictive value of field-based musculoskeletal fitness assessments in preschoolers. The purpose of this study was to determine the validity of 2 field assessments to predict peak muscle power in preschool-aged children. Four-hundred and nineteen 3- to 5-year olds participated (208 girls, 211 boys; mean age: 4.5 ± 0.9 years). Peak power (PP) was evaluated using a modified 10-s Wingate protocol as the criterion standard. Standing long-jump was measured in inches to the back of the heel using a 2-footed takeoff and landing. Shuttle-run time was measured using a shuttle-run protocol, which required children to sprint 50 feet (15.2 m), pick up a small block, and sprint back, with time measured to the closest tenth of a second. Regression modelling was used to calculate the predictive power of each field-based measurement, adjusting for weight (kg), age, and sex. Both standing long-jump distance and shuttle-run time were significantly correlated with PP (r = 0.636, p power in preschool children. Either measure can be used as a cost- and time-efficient estimate of musculoskeletal fitness in preschoolers.

  4. Simulation research on multivariable fuzzy model predictive control of nuclear power plant

    International Nuclear Information System (INIS)

    Su Jie

    2012-01-01

    To improve the dynamic control capabilities of the nuclear power plant, the algorithm of the multivariable nonlinear predictive control based on the fuzzy model was applied in the main parameters control of the nuclear power plant, including control structure and the design of controller in the base of expounding the math model of the turbine and the once-through steam generator. The simulation results show that the respond of the change of the gas turbine speed and the steam pressure under the algorithm of multivariable fuzzy model predictive control is faster than that under the PID control algorithm, and the output value of the gas turbine speed and the steam pressure under the PID control algorithm is 3%-5% more than that under the algorithm of multi-variable fuzzy model predictive control. So it shows that the algorithm of multi-variable fuzzy model predictive control can control the output of the main parameters of the nuclear power plant well and get better control effect. (author)

  5. Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2015-09-01

    Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

  6. Multivariate power-law models for streamflow prediction in the Mekong Basin

    Directory of Open Access Journals (Sweden)

    Guillaume Lacombe

    2014-11-01

    New hydrological insights for the region: A combination of 3–6 explanatory variables – chosen among annual rainfall, drainage area, perimeter, elevation, slope, drainage density and latitude – is sufficient to predict a range of flow metrics with a prediction R-squared ranging from 84 to 95%. The inclusion of forest or paddy percentage coverage as an additional explanatory variable led to slight improvements in the predictive power of some of the low-flow models (lowest prediction R-squared = 89%. A physical interpretation of the model structure was possible for most of the resulting relationships. Compared to regional regression models developed in other parts of the world, this new set of equations performs reasonably well.

  7. Application of neural networks to signal prediction in nuclear power plant

    International Nuclear Information System (INIS)

    Wan Joo Kim; Soon Heung Chang; Byung Ho Lee

    1993-01-01

    This paper describes the feasibility study of an artificial neural network for signal prediction. The purpose of signal prediction is to estimate the value of undetected next time step signal. As the prediction method, based on the idea of auto regression, a few previous signals are inputs to the artificial neural network and the signal value of next time step is estimated with the outputs of the network. The artificial neural network can be applied to the nonlinear system and answers in short time. The training algorithm is a modified backpropagation model, which can effectively reduce the training time. The target signal of the simulation is the steam generator water level, which is one of the important parameters in nuclear power plants. The simulation result shows that the predicted value follows the real trend well

  8. A Model Predictive Control-Based Power Converter System for Oscillating Water Column Wave Energy Converters

    Directory of Open Access Journals (Sweden)

    Gimara Rajapakse

    2017-10-01

    Full Text Available Despite the predictability and availability at large scale, wave energy conversion (WEC has still not become a mainstream renewable energy technology. One of the main reasons is the large variations in the extracted power which could lead to instabilities in the power grid. In addition, maintaining the speed of the turbine within optimal range under changing wave conditions is another control challenge, especially in oscillating water column (OWC type WEC systems. As a solution to the first issue, this paper proposes the direct connection of a battery bank into the dc-link of the back-to-back power converter system, thereby smoothening the power delivered to the grid. For the second issue, model predictive controllers (MPCs are developed for the rectifier and the inverter of the back-to-back converter system aiming to maintain the turbine speed within its optimum range. In addition, MPC controllers are designed to control the battery current as well, in both charging and discharging conditions. Operations of the proposed battery direct integration scheme and control solutions are verified through computer simulations. Simulation results show that the proposed integrated energy storage and control solutions are capable of delivering smooth power to the grid while maintaining the turbine speed within its optimum range under varying wave conditions.

  9. The critical power function is dependent on the duration of the predictive exercise tests chosen.

    Science.gov (United States)

    Bishop, D; Jenkins, D G; Howard, A

    1998-02-01

    The linear relationship between work accomplished (W(lim)) and time to exhaustion (t(lim)) can be described by the equation: W(lim) = a + CP x t(lim). Critical power (CP) is the slope of this line and is thought to represent a maximum rate of ATP synthesis without exhaustion, presumably an inherent characteristic of the aerobic energy system. The present investigation determined whether the choice of predictive tests would elicit significant differences in the estimated CP. Ten female physical education students completed, in random order and on consecutive days, five all-out predictive tests at preselected constant-power outputs. Predictive tests were performed on an electrically-braked cycle ergometer and power loadings were individually chosen so as to induce fatigue within approximately 1-10 mins. CP was derived by fitting the linear W(lim)-t(lim) regression and calculated three ways: 1) using the first, third and fifth W(lim)-t(lim) coordinates (I135), 2) using coordinates from the three highest power outputs (I123; mean t(lim) = 68-193 s) and 3) using coordinates from the lowest power outputs (I345; mean t(lim) = 193-485 s). Repeated measures ANOVA revealed that CPI123 (201.0+/-37.9W) > CPI135 (176.1+/-27.6W) > CPI345 (164.0+/-22.8W) (P<0.05). When the three sets of data were used to fit the hyperbolic Power-t(lim) regression, statistically significant differences between each CP were also found (P<0.05). The shorter the predictive trials, the greater the slope of the W(lim)-t(lim) regression; possibly because of the greater influence of 'aerobic inertia' on these trials. This may explain why CP has failed to represent a maximal, sustainable work rate. The present findings suggest that if CP is to represent the highest power output that an individual can maintain "for a very long time without fatigue" then CP should be calculated over a range of predictive tests in which the influence of aerobic inertia is minimised.

  10. Model Predictive Current Control for High-Power Grid-Connected Converters with Output LCL Filter

    DEFF Research Database (Denmark)

    Delpino, Hernan Anres Miranda; Teodorescu, Remus; Rodriguez, Pedro

    2009-01-01

    A model predictive control strategy for a highpower, grid connected 3-level neutral clamped point converter is presented. Power losses constraints set a limit on commutation losses so reduced switching frequency is required, thus producing low frequency current harmonics. To reduce these harmonics...... an LCL filter is used. The proposed control strategy allows control of the active and reactive power fed into the grid, reduce the switching frequency within acceptable operational margins and keep balance of the DC-link capacitor voltages while avoiding excitation of the filter resonance frequencies....

  11. Model Predictive Control of Grid Connected Modular Multilevel Converter for Integration of Photovoltaic Power Systems

    DEFF Research Database (Denmark)

    Hajizadeh, Amin; Shahirinia, Amir

    2017-01-01

    Investigation of an advanced control structure for integration of Photovoltaic Power Systems through Grid Connected-Modular Multilevel Converter (GC-MMC) is proposed in this paper. To achieve this goal, a non-linear model of MMC regarding considering of negative and positive sequence components has...... been presented. Then, due to existence of unbalance voltage faults in distribution grid, non-linarites and uncertainties in model, model predictive controller which is developed for GC-MMC. They are implemented based upon positive and negative components of voltage and current to mitigate the power...

  12. Predictions for Boson-Jet Observables and Fragmentation Function Ratios from a Hybrid Strong/Weak Coupling Model for Jet Quenching

    CERN Document Server

    Casalderrey-Solana, Jorge; Milhano, José Guilherme; Pablos, Daniel; Rajagopal, Krishna

    2016-01-01

    We have previously introduced a hybrid strong/weak coupling model for jet quenching in heavy ion collisions that describes the production and fragmentation of jets at weak coupling, using PYTHIA, and describes the rate at which each parton in the jet shower loses energy as it propagates through the strongly coupled plasma, dE/dx, using an expression computed holographically at strong coupling. The model has a single free parameter that we fit to a single experimental measurement. We then confront our model with experimental data on many other jet observables, focusing here on boson-jet observables, finding that it provides a good description of present jet data. Next, we provide the predictions of our hybrid model for many measurements to come, including those for inclusive jet, dijet, photon-jet and Z-jet observables in heavy ion collisions with energy $\\sqrt{s}=5.02$ ATeV coming soon at the LHC. As the statistical uncertainties on near-future measurements of photon-jet observables are expected to be much sm...

  13. PID and predictive control of electrical drives and power converters using MATLAB/Simulink

    CERN Document Server

    Wang, Liuping; Yoo, Dae; Gan, Lu; Ng, Ki

    2015-01-01

    A timely introduction to current research on PID and predictive control by one of the leading authors on the subject PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice.  The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis.    The book contains secti

  14. Predictive current control with instantaneous reactive power minimization for a four-leg indirect matrix converter

    OpenAIRE

    Garcia, Cristian F.; Rivera, Marco E.; Rodriguez, Jose R.; Wheeler, Patrick; Pena, Ruben S.

    2017-01-01

    This paper presents the experimental valida¬tion of a predictive current control strategy with minimiza¬tion of the instantaneous reactive input power for a Four-Leg Indirect Matrix Converter (4Leg-IMC). The topology includes an input matrix converter stage, which provides the dc voltage for a four-leg voltage source converter (VSC) output stage. The VSC’s fourth leg provides a path for the zero sequence load current. The control technique is based on a finite control set model predictive con...

  15. Prediction of Rowing Ergometer Performance from Functional Anaerobic Power, Strength and Anthropometric Components

    Directory of Open Access Journals (Sweden)

    Akça Firat

    2014-07-01

    Full Text Available The aim of this research was to develop different regression models to predict 2000 m rowing ergometer performance with the use of anthropometric, anaerobic and strength variables and to determine how precisely the prediction models constituted by different variables predict performance, when conducted together in the same equation or individually. 38 male collegiate rowers (20.17 ± 1.22 years participated in this study. Anthropometric, strength, 2000 m maximal rowing ergometer and rowing anaerobic power tests were applied. Multiple linear regression procedures were employed in SPSS 16 to constitute five different regression formulas using a different group of variables. The reliability of the regression models was expressed by R2 and the standard error of estimate (SEE. Relationships of all parameters with performance were investigated through Pearson correlation coefficients. The prediction model using a combination of anaerobic, strength and anthropometric variables was found to be the most reliable equation to predict 2000 m rowing ergometer performance (R2 = 0.92, SEE= 3.11 s. Besides, the equation that used rowing anaerobic and strength test results also provided a reliable prediction (R2 = 0.85, SEE= 4.27 s. As a conclusion, it seems clear that physiological determinants which are affected by anaerobic energy pathways should also get involved in the processes and models used for performance prediction and talent identification in rowing.

  16. Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

    2015-01-01

    , which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC......This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead...... of partial linearization of the wind turbine model at selected operating points, the nonlinearities of the wind turbine model are represented by a piece-wise static function based on the wind turbine system inputs and state variables. The nonlinearity identification is based on the clustering-based algorithm...

  17. Short-term load and wind power forecasting using neural network-based prediction intervals.

    Science.gov (United States)

    Quan, Hao; Srinivasan, Dipti; Khosravi, Abbas

    2014-02-01

    Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

  18. Artificial Neural Networks to Predict the Power Output of a PV Panel

    Directory of Open Access Journals (Sweden)

    Valerio Lo Brano

    2014-01-01

    Full Text Available The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs for the power energy output forecasting of photovoltaic (PV modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP, a recursive neural network (RNN, and a gamma memory (GM trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

  19. Improving the predictive accuracy of hurricane power outage forecasts using generalized additive models.

    Science.gov (United States)

    Han, Seung-Ryong; Guikema, Seth D; Quiring, Steven M

    2009-10-01

    Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.

  20. Steady-state plant model to predict hydrogen levels in power plant components

    Science.gov (United States)

    Glatzmaier, Greg C.; Cable, Robert; Newmarker, Marc

    2017-06-01

    The National Renewable Energy Laboratory (NREL) and Acciona Energy North America developed a full-plant steady-state computational model that estimates levels of hydrogen in parabolic trough power plant components. The model estimated dissolved hydrogen concentrations in the circulating heat transfer fluid (HTF), and corresponding partial pressures within each component. Additionally for collector field receivers, the model estimated hydrogen pressure in the receiver annuli. The model was developed to estimate long-term equilibrium hydrogen levels in power plant components, and to predict the benefit of hydrogen mitigation strategies for commercial power plants. Specifically, the model predicted reductions in hydrogen levels within the circulating HTF that result from purging hydrogen from the power plant expansion tanks at a specified target rate. Our model predicted hydrogen partial pressures from 8.3 mbar to 9.6 mbar in the power plant components when no mitigation treatment was employed at the expansion tanks. Hydrogen pressures in the receiver annuli were 8.3 to 8.4 mbar. When hydrogen partial pressure was reduced to 0.001 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.001 mbar to 0.02 mbar. When hydrogen partial pressure was reduced to 0.3 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.25 mbar to 0.28 mbar. Our results show that controlling hydrogen partial pressure in the expansion tanks allows us to reduce and maintain hydrogen pressures in the receiver annuli to any practical level.

  1. Steady-State Plant Model to Predict Hydroden Levels in Power Plant Components

    Energy Technology Data Exchange (ETDEWEB)

    Glatzmaier, Greg C.; Cable, Robert; Newmarker, Marc

    2017-06-27

    The National Renewable Energy Laboratory (NREL) and Acciona Energy North America developed a full-plant steady-state computational model that estimates levels of hydrogen in parabolic trough power plant components. The model estimated dissolved hydrogen concentrations in the circulating heat transfer fluid (HTF), and corresponding partial pressures within each component. Additionally for collector field receivers, the model estimated hydrogen pressure in the receiver annuli. The model was developed to estimate long-term equilibrium hydrogen levels in power plant components, and to predict the benefit of hydrogen mitigation strategies for commercial power plants. Specifically, the model predicted reductions in hydrogen levels within the circulating HTF that result from purging hydrogen from the power plant expansion tanks at a specified target rate. Our model predicted hydrogen partial pressures from 8.3 mbar to 9.6 mbar in the power plant components when no mitigation treatment was employed at the expansion tanks. Hydrogen pressures in the receiver annuli were 8.3 to 8.4 mbar. When hydrogen partial pressure was reduced to 0.001 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.001 mbar to 0.02 mbar. When hydrogen partial pressure was reduced to 0.3 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.25 mbar to 0.28 mbar. Our results show that controlling hydrogen partial pressure in the expansion tanks allows us to reduce and maintain hydrogen pressures in the receiver annuli to any practical level.

  2. Battery available power prediction of hybrid electric vehicle based on improved Dynamic Matrix Control algorithms

    Science.gov (United States)

    Wang, Limei; Cheng, Yong; Zou, Ju

    2014-09-01

    The core technology to any hybrid engine vehicle (HEV) is the design of energy management strategy (EMS). To develop a reasonable EMS, it is necessary to monitor the state of capacity, state of health and instantaneous available power of battery packs. A new method that linearizes RC equivalent circuit model and predicts battery available power according to original Dynamic Matrix Control algorithm is proposed. To verify the validity of the new algorithm, a bench test with lithium-ion battery cell and a HEV test with lithium-ion battery packs are carried out. The bench test results indicate that a single RC block equivalent circuit model could be used to describe the dynamic and the steady state characteristics of a battery under testing conditions. However, lacking of long time constant of RC modules, there is a sample deviation in the open-circuit voltage identified and that measured. The HEV testing results show that the battery voltage predicted is in good agreement with that measured, the maximum difference is within 3.7%. Fixing the time constant to a numeric value, satisfactory results can still be achieved. After setting a battery discharge cut-off voltage, the instantaneous available power of the battery can be predicted.

  3. Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power

    Directory of Open Access Journals (Sweden)

    Mashud Rana

    2016-10-01

    Full Text Available Solar energy generated from PhotoVoltaic (PV systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the electricity generated by PV systems. In this paper we consider 2D-interval forecasts, where the goal is to predict summary statistics for the distribution of the PV power values in a future time interval. 2D-interval forecasts have been recently introduced, and they are more suitable than point forecasts for applications where the predicted variable has a high variability. We propose a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles. NNE2D was evaluated for univariate prediction of Australian solar PV power data for two years. The results show that it is a promising method, outperforming persistence baselines and other methods used for comparison in terms of accuracy and coverage probability.

  4. Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2016-01-01

    Full Text Available Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

  5. Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology

    Directory of Open Access Journals (Sweden)

    M. Hameedullah

    2010-01-01

    Full Text Available Power consumption in turning EN-31 steel (a material that is most extensively used in automotive industry with tungstencarbide tool under different cutting conditions was experimentally investigated. The experimental runs were planned accordingto 24+8 added centre point factorial design of experiments, replicated thrice. The data collected was statisticallyanalyzed using Analysis of Variance technique and first order and second order power consumption prediction models weredeveloped by using response surface methodology (RSM. It is concluded that second-order model is more accurate than thefirst-order model and fit well with the experimental data. The model can be used in the automotive industries for decidingthe cutting parameters for minimum power consumption and hence maximum productivity

  6. Drag prediction method of powered-on civil aircraft based on thrust drag bookkeeping

    Directory of Open Access Journals (Sweden)

    Zhang Yufei

    2015-08-01

    Full Text Available A drag prediction method based on thrust drag bookkeeping (TDB is introduced for civil jet propulsion/airframe integration performance analysis. The method is derived from the control volume theory of a powered-on nacelle. Key problem of the TDB is identified to be accurate prediction of velocity coefficient of the powered-on nacelle. Accuracy of CFD solver is validated by test cases of the first AIAA Propulsion Aerodynamics Workshop. Then the TDB method is applied to thrust and drag decomposing of a realistic aircraft. A linear relation between the computations assumed free stream Mach number and the velocity coefficient result is revealed. The thrust losses caused by nozzle internal drag and pylon scrubbing are obtained by the isolated nacelle and mapped on to the in-flight whole configuration analysis. Effects of the powered-on condition are investigated by comparing through-flow configuration with powered-on configuration. The variance on aerodynamic coefficients and pressure distribution is numerically studied.

  7. Performance Prediction of Active Piezo Fiber Rackets in Terms of Tennis Power

    Science.gov (United States)

    Kawazoe, Yoshihiko; Takeda, Yukihiro; Nakagawa, Masamichi

    Several former top players sent a letter to the International Tennis Federation (ITF) encouraging the governing body to revisit the question of rackets. In the letter, the players wrote that racket technology has led to major changes in how the game is played at the top level. This paper investigated the physical properties of a new type of racket with active piezoelectric fibers appeared recently in the market, and predicted the various factors associated with the frontal impact, such as impact force, contact time, deformation of ball and strings, and also estimated the racket performance such as the coefficient of restitution, the rebound power coefficient, the post-impact ball velocity and the sweet areas relevant to the power in tennis. It is based on the experimental identification of the dynamics of the ball-racket-arm system and the approximate nonlinear impact analysis with a simple swing model. The predicted results with forehand stroke model can explain the difference in mechanism of performance between the new type racket with active piezoelectric fibers and the conventional passive representative rackets. It showed that this new type racket provides higher coefficient of restitution on the whole area of string face and also gives larger rebound power coefficients particularly at the topside and bigger powers on the whole area of string face but the difference was not so large. It seems that the racket-related improvements in play are relatively small and the players themselves continue to improve, accordingly there is a gap between a perception and reality.

  8. Intraocular Lens Power Calculation after Refractive Surgery: A Comparative Analysis of Accuracy and Predictability.

    Science.gov (United States)

    Kang, Byeong Soo; Han, Jeong Mo; Oh, Joo Youn; Kim, Mee Kum; Wee, Won Ryang

    2017-12-01

    To compare the accuracy of intraocular lens (IOL) power calculation using conventional regression formulae or the American Society of Cataract and Refractive Surgery (ASCRS) IOL power calculator for previous corneal refractive surgery. We retrospectively reviewed 96 eyes from 68 patients that had undergone cataract surgery after keratorefractive surgeries. We calculated the formula with two approaches: IOL powers using the ASCRS IOL power calculator and IOL powers using conventional formulae with previous refractive data (Camellin, Jarade, Savini, and clinical history method) or without prior data (0, 2 and, 4 mm total mean power in topography, Wang-Koch-Maloney, Shammas, Seitz, and Maloney). Two conventional IOL formulae (the SRK/T and the Hoffer Q) were calculated with the single K and double K methods. Mean arithmetic refractive error and mean absolute error were calculated at the first postoperative month. In conventional formulae, the Jarade method or the Seitz method, applied in the Hoffer Q formula with the single K or double K method, have the lowest prediction errors. The least prediction error was found in the Shammas-PL method in the ASCRS group. There was no statistically significant difference between the 10 lowest mean absolute error conventional methods, the Shammas-PL method and the Barrett True-K method calculated with using the ASCRS calculator, without using preoperative data. The Shammas-PL formula and the Barrett True-K formula, calculated with the ASCRS calculator, without using history, were methods comparable to the 10 most accurate conventional formulae. Other methods using the ASCRS calculator show a myopic tendency. © 2017 The Korean Ophthalmological Society

  9. Conceptual Software Reliability Prediction Models for Nuclear Power Plant Safety Systems

    International Nuclear Information System (INIS)

    Johnson, G.; Lawrence, D.; Yu, H.

    2000-01-01

    The objective of this project is to develop a method to predict the potential reliability of software to be used in a digital system instrumentation and control system. The reliability prediction is to make use of existing measures of software reliability such as those described in IEEE Std 982 and 982.2. This prediction must be of sufficient accuracy to provide a value for uncertainty that could be used in a nuclear power plant probabilistic risk assessment (PRA). For the purposes of the project, reliability was defined to be the probability that the digital system will successfully perform its intended safety function (for the distribution of conditions under which it is expected to respond) upon demand with no unintended functions that might affect system safety. The ultimate objective is to use the identified measures to develop a method for predicting the potential quantitative reliability of a digital system. The reliability prediction models proposed in this report are conceptual in nature. That is, possible prediction techniques are proposed and trial models are built, but in order to become a useful tool for predicting reliability, the models must be tested, modified according to the results, and validated. Using methods outlined by this project, models could be constructed to develop reliability estimates for elements of software systems. This would require careful review and refinement of the models, development of model parameters from actual experience data or expert elicitation, and careful validation. By combining these reliability estimates (generated from the validated models for the constituent parts) in structural software models, the reliability of the software system could then be predicted. Modeling digital system reliability will also require that methods be developed for combining reliability estimates for hardware and software. System structural models must also be developed in order to predict system reliability based upon the reliability

  10. Computerized heat balance models to predict performance of operating nuclear power plants

    International Nuclear Information System (INIS)

    Breeding, C.L.; Carter, J.C.; Schaefer, R.C.

    1983-01-01

    The use of computerized heat balance models has greatly enhanced the decision making ability of TVA's Division of Nuclear Power. These models are utilized to predict the effects of various operating modes and to analyze changes in plant performance resulting from turbine cycle equipment modifications with greater speed and accuracy than was possible before. Computer models have been successfully used to optimize plant output by predicting the effects of abnormal condenser circulating water conditions. They were utilized to predict the degradation in performance resulting from installation of a baffle plate assembly to replace damaged low-pressure blading, thereby providing timely information allowing an optimal economic judgement as to when to replace the blading. Future use will be for routine performance test analysis. This paper presents the benefits of utility use of computerized heat balance models

  11. Statistical prediction of the numbers of degraded tubes in nuclear power plant steam generators

    International Nuclear Information System (INIS)

    Gallucci, R.H.V.; Klisiewicz, J.W.; Craig, K.R.

    1990-01-01

    Corrosion of nuclear power plant steam generator (SG) tubes often necessitates plugging/sleeving, causing decreased SG thermal performance and possible SG replacement. Statistical methods have been developed to predict probabilistically the numbers of tubes degraded due to secondary side pitting, wastage, and intergranular attack/stress-corrosion cracking. Inspection data from two Combustion Engineering (C-E) plants have been converted into statistics representing defect formation and growth. Computer simulation programs have been generated to predict the numbers of tubes to be plugged/sleeved during future outages. The probabilistic predictions for both plants successfully have bounded subsequent observations. While so far applied only to C-E SGs for the three degradation phenomena, the statistical methodology is adaptable to other SG types and phenomena

  12. Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler

    Directory of Open Access Journals (Sweden)

    Zhenhao Tang

    2017-01-01

    Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.

  13. Kicking Back Cognitive Ageing: Leg Power Predicts Cognitive Ageing after Ten Years in Older Female Twins.

    Science.gov (United States)

    Steves, Claire J; Mehta, Mitul M; Jackson, Stephen H D; Spector, Tim D

    2016-01-01

    Many observational studies have shown a protective effect of physical activity on cognitive ageing, but interventional studies have been less convincing. This may be due to short time scales of interventions, suboptimal interventional regimes or lack of lasting effect. Confounding through common genetic and developmental causes is also possible. We aimed to test whether muscle fitness (measured by leg power) could predict cognitive change in a healthy older population over a 10-year time interval, how this performed alongside other predictors of cognitive ageing, and whether this effect was confounded by factors shared by twins. In addition, we investigated whether differences in leg power were predictive of differences in brain structure and function after 12 years of follow-up in identical twin pairs. A total of 324 healthy female twins (average age at baseline 55, range 43-73) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB) at two time points 10 years apart. Linear regression modelling was used to assess the relationships between baseline leg power, physical activity and subsequent cognitive change, adjusting comprehensively for baseline covariates (including heart disease, diabetes, blood pressure, fasting blood glucose, lipids, diet, body habitus, smoking and alcohol habits, reading IQ, socioeconomic status and birthweight). A discordant twin approach was used to adjust for factors shared by twins. A subset of monozygotic pairs then underwent magnetic resonance imaging. The relationship between muscle fitness and brain structure and function was assessed using linear regression modelling and paired t tests. A striking protective relationship was found between muscle fitness (leg power) and both 10-year cognitive change [fully adjusted model standardised β-coefficient (Stdβ) = 0.174, p = 0.002] and subsequent total grey matter (Stdβ = 0.362, p = 0.005). These effects were robust in discordant twin analyses, where within

  14. Body image dissatisfaction in pregnant and non-pregnant females is strongly predicted by immune activation and mucosa-derived activation of the tryptophan catabolite (TRYCAT) pathway.

    Science.gov (United States)

    Roomruangwong, Chutima; Kanchanatawan, Buranee; Carvalho, André F; Sirivichayakul, Sunee; Duleu, Sebastien; Geffard, Michel; Maes, Michael

    2018-04-01

    The aim of the present study is to delineate the associations between body image dissatisfaction in pregnant women and immune-inflammatory biomarkers, i.e., C-reactive protein (CRP), zinc and IgA/IgM responses to tryptophan and tryptophan catabolites (TRYCATs). We assessed 49 pregnant and 24 non-pregnant females and assessed Body Image Satisfaction (BIS) scores at the end of term (T1), and 2-4 days (T2) and 4-6 weeks (T3) after delivery. Subjects were divided in those with a lowered BIS score (≤ 3) versus those with a higher score. Logistic regression analysis showed that a lowered T1 BIS score was predicted by CRP levels and IgA responses to tryptophan (negative) and TRYCATs (positive), perinatal depression, body mass index (BMI) and age. The sum of quinolinic acid, kynurenine, 3-OH-kynurenine and 3-OH-anthranilic acid (reflecting brain quinolinic acid contents) was the single best predictor. In addition, a large part of the variance in the T1, T2 and T3 BIS scores was explained by IgA responses to tryptophan and TRYCATs, especially quinolinic acid. Body image dissatisfaction is strongly associated with inflammation and mucosa-derived IDO activation independently from depression, pregnancy, BMI and age. IgA responses to peripheral TRYCATs, which determine brain quinolinic acid concentrations, also predict body image dissatisfaction.

  15. Midregional-proAtrial Natriuretic Peptide and High Sensitive Troponin T Strongly Predict Adverse Outcome in Patients Undergoing Percutaneous Repair of Mitral Valve Regurgitation.

    Directory of Open Access Journals (Sweden)

    Jochen Wöhrle

    Full Text Available It is not known whether biomarkers of hemodynamic stress, myocardial necrosis, and renal function might predict adverse outcome in patients undergoing percutaneous repair of severe mitral valve insufficiency. Thus, we aimed to assess the predictive value of various established and emerging biomarkers for major adverse cardiovascular events (MACE in these patients.Thirty-four patients with symptomatic severe mitral valve insufficiency with a mean STS-Score for mortality of 12.6% and a mean logistic EuroSCORE of 19.7% undergoing MitraClip therapy were prospectively included in this study. Plasma concentrations of mid regional-proatrial natriuretic peptide (MR-proANP, Cystatin C, high-sensitive C-reactive protein (hsCRP, high-sensitive troponin T (hsTnT, N-terminal B-type natriuretic peptide (NT-proBNP, galectin-3, and soluble ST-2 (interleukin 1 receptor-like 1 were measured directly before procedure. MACE was defined as cardiovascular death and hospitalization for heart failure (HF.During a median follow-up of 211 days (interquartile range 133 to 333 days, 9 patients (26.5% experienced MACE (death: 7 patients, rehospitalization for HF: 2 patients. Thirty day MACE-rate was 5.9% (death: 2 patients, no rehospitalization for HF. Baseline concentrations of hsTnT (Median 92.6 vs 25.2 ng/L, NT-proBNP (Median 11251 vs 1974 pg/mL and MR-proANP (Median 755.6 vs 318.3 pmol/L, all p<0.001 were clearly higher in those experiencing an event vs event-free patients, while other clinical variables including STS-Score and logistic EuroSCORE did not differ significantly. In Kaplan-Meier analyses, NT-proBNP and in particular hsTnT and MR-proANP above the median discriminated between those experiencing an event vs event-free patients. This was further corroborated by C-statistics where areas under the ROC curve for prediction of MACE using the respective median values were 0.960 for MR-proANP, 0.907 for NT-proBNP, and 0.822 for hsTnT.MR-proANP and hsTnT strongly

  16. THE SYSTEMATICS OF STRONG LENS MODELING QUANTIFIED: THE EFFECTS OF CONSTRAINT SELECTION AND REDSHIFT INFORMATION ON MAGNIFICATION, MASS, AND MULTIPLE IMAGE PREDICTABILITY

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, Traci L.; Sharon, Keren, E-mail: tljohn@umich.edu [University of Michigan, Department of Astronomy, 1085 South University Avenue, Ann Arbor, MI 48109-1107 (United States)

    2016-11-20

    Until now, systematic errors in strong gravitational lens modeling have been acknowledged but have never been fully quantified. Here, we launch an investigation into the systematics induced by constraint selection. We model the simulated cluster Ares 362 times using random selections of image systems with and without spectroscopic redshifts and quantify the systematics using several diagnostics: image predictability, accuracy of model-predicted redshifts, enclosed mass, and magnification. We find that for models with >15 image systems, the image plane rms does not decrease significantly when more systems are added; however, the rms values quoted in the literature may be misleading as to the ability of a model to predict new multiple images. The mass is well constrained near the Einstein radius in all cases, and systematic error drops to <2% for models using >10 image systems. Magnification errors are smallest along the straight portions of the critical curve, and the value of the magnification is systematically lower near curved portions. For >15 systems, the systematic error on magnification is ∼2%. We report no trend in magnification error with the fraction of spectroscopic image systems when selecting constraints at random; however, when using the same selection of constraints, increasing this fraction up to ∼0.5 will increase model accuracy. The results suggest that the selection of constraints, rather than quantity alone, determines the accuracy of the magnification. We note that spectroscopic follow-up of at least a few image systems is crucial because models without any spectroscopic redshifts are inaccurate across all of our diagnostics.

  17. Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

    Energy Technology Data Exchange (ETDEWEB)

    Cipcigan, Flaviu S., E-mail: flaviu.cipcigan@ed.ac.uk [School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD (United Kingdom); National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW (United Kingdom); Sokhan, Vlad P. [National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW (United Kingdom); Crain, Jason [School of Physics and Astronomy, University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD (United Kingdom); National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW (United Kingdom); Martyna, Glenn J. [IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 (United States)

    2016-12-01

    One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082–1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230–233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeler through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO-MD. - Highlights: • Electronic coarse graining unites many-body dispersion and polarisation beyond the dipole limit. • It consists of replacing the electrons of a molecule using a quantum harmonic oscillator, called a

  18. Predicting transmission of structure-borne sound power from machines by including terminal cross-coupling

    Science.gov (United States)

    Ohlrich, Mogens

    2011-10-01

    Structure-borne sound generated by audible vibration of machines in vehicles, equipment and house-hold appliances is often a major cause of noise. Such vibration of complex machines is mostly determined and quantified by measurements. It has been found that characterization of the vibratory source strength and the prediction of power transmission to a supporting structure or the machine casing itself can be greatly simplified if all mobility cross-terms and spatial cross-coupling of source velocities can be neglected in the analysis. In many cases this gives an acceptable engineering accuracy, especially at mid- and high-frequencies. For structurally compact machines, however, the influence of cross-coupling cannot always be ignored. The present paper addresses this problem and examines the transmission of structure-borne sound power by including spatial cross-coupling between pairs of translational terminals in a global plane. This paired or bi-coupled power transmission represents the simplest case of cross-coupling. The procedure and quality of the predicted transmission using this improved technique is demonstrated experimentally for an electrical motor unit with an integrated radial fan that was mounted resiliently in a vacuum cleaner casing. It is found that cross-coupling plays a significant role, but only at frequencies below 100 Hz for the examined system.

  19. Method for predicting homology modeling accuracy from amino acid sequence alignment: the power function.

    Science.gov (United States)

    Iwadate, Mitsuo; Kanou, Kazuhiko; Terashi, Genki; Umeyama, Hideaki; Takeda-Shitaka, Mayuko

    2010-01-01

    We have devised a power function (PF) that can predict the accuracy of a three-dimensional (3D) structure model of a protein using only amino acid sequence alignments. This Power Function (PF) consists of three parts; (1) the length of a model, (2) a homology identity percent value and (3) the agreement rate between PSI-PRED secondary structure prediction and the secondary structure judgment of a reference protein. The PF value is mathematically computed from the execution process of homology search tools, such as FASTA or various BLAST programs, to obtain the amino acid sequence alignments. There is a high correlation between the global distance test-total score (GDT_TS) value of the protein model based upon the PF score and the GDT_TS(MAX) value used as an index of protein modeling accuracy in the international contest Critical Assessment of Techniques for Protein Structure Prediction (CASP). Accordingly, the PF method is valuable for constructing a highly accurate model without wasteful calculations of homology modeling that is normally performed by an iterative method to move the main chain and side chains in the modeling process. Moreover, a model with higher accuracy can be obtained by combining the models ordered by the PF score with models sorted by the size of the CIRCLE score. The CIRCLE software is a 3D-1D program, in which energetic stabilization is estimated based upon the experimental environment of each amino acid residue in the protein solution or protein crystals.

  20. How Many Model Evaluations Are Required To Predict The AEP Of A Wind Power Plant?

    International Nuclear Information System (INIS)

    Murcia, J P; Réthoré, P E; Natarajan, A; Sørensen, J D

    2015-01-01

    Wind farm flow models have advanced considerably with the use of large eddy simulations (LES) and Reynolds averaged Navier-Stokes (RANS) computations. The main limitation of these techniques is their high computational time requirements; which makes their use for wind farm annual energy production (AEP) predictions expensive. The objective of the present paper is to minimize the number of model evaluations required to capture the wind power plant's AEP using stationary wind farm flow models. Polynomial chaos techniques are proposed based on arbitrary Weibull distributed wind speed and Von Misses distributed wind direction. The correlation between wind direction and wind speed are captured by defining Weibull-parameters as functions of wind direction. In order to evaluate the accuracy of these methods the expectation and variance of the wind farm power distributions are compared against the traditional binning method with trapezoidal and Simpson's integration rules.The wind farm flow model used in this study is the semi-empirical wake model developed by Larsen [1]. Three test cases are studied: a single turbine, a simple and a real offshore wind power plant. A reduced number of model evaluations for a general wind power plant is proposed based on the convergence of the present method for each case. (paper)

  1. A Study of Performance in Low-Power Tokamak Reactor with Integrated Predictive Modeling Code

    International Nuclear Information System (INIS)

    Pianroj, Y.; Onjun, T.; Suwanna, S.; Picha, R.; Poolyarat, N.

    2009-07-01

    Full text: A fusion hybrid or a small fusion power output with low power tokamak reactor is presented as another useful application of nuclear fusion. Such tokamak can be used for fuel breeding, high-level waste transmutation, hydrogen production at high temperature, and testing of nuclear fusion technology components. In this work, an investigation of the plasma performance in a small fusion power output design is carried out using the BALDUR predictive integrated modeling code. The simulations of the plasma performance in this design are carried out using the empirical-based Mixed Bohm/gyro Bohm (B/gB) model, whereas the pedestal temperature model is based on magnetic and flow shear (δ α ρ ζ 2 ) stabilization pedestal width scaling. The preliminary results using this core transport model show that the central ion and electron temperatures are rather pessimistic. To improve the performance, the optimization approach are carried out by varying some parameters, such as plasma current and power auxiliary heating, which results in some improvement of plasma performance

  2. Predicting transmission of structure-borne sound power from machines by including terminal cross-coupling

    DEFF Research Database (Denmark)

    Ohlrich, Mogens

    2011-01-01

    strength and the prediction of power transmission to a supporting structure or the machine casing itself can be greatly simplified if all mobility cross-terms and spatial cross-coupling of source velocities can be neglected in the analysis. In many cases this gives an acceptable engineering accuracy......, especially at mid- and high-frequencies. For structurally compact machines, however, the influence of cross-coupling cannot always be ignored. The present paper addresses this problem and examines the transmission of structure-borne sound power by including spatial cross-coupling between pairs......Structure-borne sound generated by audible vibration of machines in vehicles, equipment and house-hold appliances is often a major cause of noise. Such vibration of complex machines is mostly determined and quantified by measurements. It has been found that characterization of the vibratory source...

  3. How Many Model Evaluations Are Required To Predict The AEP Of A Wind Power Plant?

    DEFF Research Database (Denmark)

    Murcia Leon, Juan Pablo; Réthoré, Pierre-Elouan; Natarajan, Anand

    2015-01-01

    (AEP) predictions expensive. The objective of the present paper is to minimize the number of model evaluations required to capture the wind power plant's AEP using stationary wind farm flow models. Polynomial chaos techniques are proposed based on arbitrary Weibull distributed wind speed and Von Misses......Wind farm flow models have advanced considerably with the use of large eddy simulations (LES) and Reynolds averaged Navier-Stokes (RANS) computations. The main limitation of these techniques is their high computational time requirements; which makes their use for wind farm annual energy production...... distributed wind direction. The correlation between wind direction and wind speed are captured by defining Weibull-parameters as functions of wind direction. In order to evaluate the accuracy of these methods the expectation and variance of the wind farm power distributions are compared against...

  4. Discrete Model Predictive Control-Based Maximum Power Point Tracking for PV Systems: Overview and Evaluation

    DEFF Research Database (Denmark)

    Lashab, Abderezak; Sera, Dezso; Guerrero, Josep M.

    2018-01-01

    The main objective of this work is to provide an overview and evaluation of discrete model predictive controlbased maximum power point tracking (MPPT) for PV systems. A large number of MPC based MPPT methods have been recently introduced in the literature with very promising performance, however......, an in-depth investigation and comparison of these methods have not been carried out yet. Therefore, this paper has set out to provide an in-depth analysis and evaluation of MPC based MPPT methods applied to various common power converter topologies. The performance of MPC based MPPT is directly linked...... with the converter topology, and it is also affected by the accurate determination of the converter parameters, sensitivity to converter parameter variations is also investigated. The static and dynamic performance of the trackers are assessed according to the EN 50530 standard, using detailed simulation models...

  5. Prediction of radio frequency power generation of Neptune's magnetosphere from generalized radiometric Bode's law

    Science.gov (United States)

    Million, M. A.; Goertz, C. K.

    1988-01-01

    Magnetospheric radio frequency emission power has been shown to vary as a function of both solar wind and planetary values such as magnetic field by Kaiser and Desch (1984). Planetary magnetic fields have been shown to scale with planetary variables such as density and angular momentum by numerous researchers. This paper combines two magnetic scaling laws with the radiometric law to yield 'Bode's'-type laws governing planetary radio emissions. Further analysis allows the reduction of variables to planetary mass and orbital distance. These generalized laws are then used to predict the power otuput of Neptune to be about 1.6 x 10 to the 7th W; with the intensity peaking at about 3 MHz.

  6. Application of an estimation model to predict future transients at US nuclear power plants

    International Nuclear Information System (INIS)

    Hallbert, B.P.; Blackman, H.S.

    1987-01-01

    A model developed by R.A. Fisher was applied to a set of Licensee Event Reports (LERs) summarizing transient initiating events at US commercial nuclear power plants. The empirical Bayes model was examined to study the feasibility of estimating the number of categories of transients which have not yet occurred at nuclear power plants. An examination of the model's predictive ability using an existing sample of data provided support for use of the model to estimate future transients. The estimate indicates that an approximate fifteen percent increase in the number of categories of transient initiating events may be expected during the period 1983--1993, assuming a stable process of transients. Limitations of the model and other possible applications are discussed. 10 refs., 1 fig., 3 tabs

  7. Model Predictive Control for Flexible Power Consumption of Large-Scale Refrigeration Systems

    DEFF Research Database (Denmark)

    Shafiei, Seyed Ehsan; Stoustrup, Jakob; Rasmussen, Henrik

    2014-01-01

    A model predictive control (MPC) scheme is introduced to directly control the electrical power consumption of large-scale refrigeration systems. Deviation from the baseline of the consumption is corresponded to the storing and delivering of thermal energy. By virtue of such correspondence......, the control method can be employed for regulating power services in the smart grid. The proposed scheme contains the control of cooling capacity as well as optimizing the efficiency factor of the system, which is in general a nonconvex optimization problem. By introducing a fictitious manipulated variable......, and novel incorporation of the evaporation temperature set-point into optimization problem, the convex optimization problem is formulated within the MPC scheme. The method is applied to a simulation benchmark of large-scale refrigeration systems including several medium and low temperature cold reservoirs....

  8. Computational models for residual creep life prediction of power plant components

    International Nuclear Information System (INIS)

    Grewal, G.S.; Singh, A.K.; Ramamoortry, M.

    2006-01-01

    All high temperature - high pressure power plant components are prone to irreversible visco-plastic deformation by the phenomenon of creep. The steady state creep response as well as the total creep life of a material is related to the operational component temperature through, respectively, the exponential and inverse exponential relationships. Minor increases in the component temperature can thus have serious consequences as far as the creep life and dimensional stability of a plant component are concerned. In high temperature steam tubing in power plants, one mechanism by which a significant temperature rise can occur is by the growth of a thermally insulating oxide film on its steam side surface. In the present paper, an elegantly simple and computationally efficient technique is presented for predicting the residual creep life of steel components subjected to continual steam side oxide film growth. Similarly, fabrication of high temperature power plant components involves extensive use of welding as the fabrication process of choice. Naturally, issues related to the creep life of weldments have to be seriously addressed for safe and continual operation of the welded plant component. Unfortunately, a typical weldment in an engineering structure is a zone of complex microstructural gradation comprising of a number of distinct sub-zones with distinct meso-scale and micro-scale morphology of the phases and (even) chemistry and its creep life prediction presents considerable challenges. The present paper presents a stochastic algorithm, which can be' used for developing experimental creep-cavitation intensity versus residual life correlations for welded structures. Apart from estimates of the residual life in a mean field sense, the model can be used for predicting the reliability of the plant component in a rigorous probabilistic setting. (author)

  9. Application of numerical weather prediction in wind power forecasting: Assessment of the diurnal cycle

    Directory of Open Access Journals (Sweden)

    Tobias Heppelmann

    2017-06-01

    Full Text Available For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP. With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD, against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

  10. Active power filter for medium voltage networks with predictive current control

    Energy Technology Data Exchange (ETDEWEB)

    Verne, Santiago A.; Valla, Maria I. [Laboratorio de Electronica Industrial, Control e Instrumentacion (LEICI), Facultad de Ingenieria, Universidad Nacional de La Plata and CONICET, La Plata (Argentina)

    2010-12-15

    A transformer less Shunt Active Power Filter (SAPF) for medium voltage distribution networks based on Multilevel Diode Clamped Inverter is presented in this paper. Converter current control is based on a Model Predictive strategy, which gives very fast current response. Also, the algorithm includes voltage balancing capability which is essential for proper converter operation. The presented current control algorithm is naturally applicable to converters with an arbitrary number of levels with reduced computational effort by virtue of the incorporation of switching restrictions which are necessary for reliable converter operation. The performance of the proposed algorithm is evaluated by means of computer simulations. (author)

  11. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    International Nuclear Information System (INIS)

    Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van’t

    2012-01-01

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  12. Impact of Statistical Learning Methods on the Predictive Power of Multivariate Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van' t [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands)

    2012-03-15

    Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.

  13. Nuclear power plant maintenance personnel reliability prediction (NPP/MPRP) effort at Oak Ridge National Laboratory

    International Nuclear Information System (INIS)

    Knee, H.E.; Haas, P.M.; Siegel, A.I.

    1981-01-01

    Human errors committed during maintenance activities are potentially a major contribution to the overall risk associated with the operation of a nuclear power plant (NPP). An NRC-sponsored program at Oak Ridge National Laboratory is attempting to develop a quantitative predictive technique to evaluate the contribution of maintenance errors to the overall NPP risk. The current work includes a survey of the requirements of potential users to ascertain the need for and content of the proposed quantitative model, plus an initial job/task analysis to determine the scope and applicability of various maintenance tasks. In addition, existing human reliability prediction models are being reviewed and assessed with respect to their applicability to NPP maintenance tasks. This paper discusses the status of the program and summarizes the results to date

  14. Active Power Optimal Control of Wind Turbines with Doubly Fed Inductive Generators Based on Model Predictive Control

    OpenAIRE

    Guo Jiuwang; Liu Xingjie; Wei Wang

    2015-01-01

    Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF) wind power generation system with doubly fed induction generators (DFIG), traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing...

  15. Power Transformer Operating State Prediction Method Based on an LSTM Network

    Directory of Open Access Journals (Sweden)

    Hui Song

    2018-04-01

    Full Text Available The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

  16. Short-term prediction of windfarm power output - from theory to practice

    International Nuclear Information System (INIS)

    Landberg, L.

    1998-01-01

    From the very complicated and evolved theories of boundary-layer meteorology encompassing the equations of turbulence and mean flow, a model has been derived to predict the power output from wind farms. For practical dispatching purposes the predictions must reach as far into the future as 36 hours. The model has been put into an operation frame-work where the predictions for a number of wind farms scattered all over Europe are available on-line on the World Wide Web. The system is very versatile and new wind farms can be included within a few days. The system is made up of predictions from the Danish Meteorological Institute HIRLAM model which are refined using the WASP model from Risoe National Laboratory. The paper will describe this operation set-up, give examples of the performance of the model of wind farms in the UK, Denmark, Greece and the US. An analysis of the error for a one-year period will also be presented. Finally, possible improvements will be discussed. These include Kalman filtering and other statistical methods. (Author)

  17. The Predictive Power of Evolutionary Biology and the Discovery of Eusociality in the Naked Mole-Rat.

    Science.gov (United States)

    Braude, Stanton

    1997-01-01

    Discusses how biologists use evolutionary theory and provides examples of how evolutionary biologists test hypotheses on specific modes of selection and evolution. Presents an example of the successful predictive power of one evolutionary hypothesis. Contains 38 references. (DDR)

  18. A GLOBAL ASSESSMENT OF SOLAR ENERGY RESOURCES: NASA's Prediction of Worldwide Energy Resources (POWER) Project

    Science.gov (United States)

    Zhang, T.; Stackhouse, P. W., Jr.; Chandler, W.; Hoell, J. M.; Westberg, D.; Whitlock, C. H.

    2010-12-01

    NASA's POWER project, or the Prediction of the Worldwide Energy Resources project, synthesizes and analyzes data on a global scale. The products of the project find valuable applications in the solar and wind energy sectors of the renewable energy industries. The primary source data for the POWER project are NASA's World Climate Research Project (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Release 3.0) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (V 4.0.3). Users of the POWER products access the data through NASA's Surface meteorology and Solar Energy (SSE, Version 6.0) website (http://power.larc.nasa.gov). Over 200 parameters are available to the users. The spatial resolution is 1 degree by 1 degree now and will be finer later. The data covers from July 1983 to December 2007, a time-span of 24.5 years, and are provided as 3-hourly, daily and monthly means. As of now, there have been over 18 million web hits and over 4 million data file downloads. The POWER products have been systematically validated against ground-based measurements, and in particular, data from the Baseline Surface Radiation Network (BSRN) archive, and also against the National Solar Radiation Data Base (NSRDB). Parameters such as minimum, maximum, daily mean temperature and dew points, relative humidity and surface pressure are validated against the National Climate Data Center (NCDC) data. SSE feeds data directly into Decision Support Systems including RETScreen International clean energy project analysis software that is written in 36 languages and has greater than 260,000 users worldwide.

  19. HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

    Science.gov (United States)

    Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

    2015-04-01

    In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the downscaling and post-processing of ensemble extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological ensemble predictions, post-processing techniques need to be tested in order to improve the quality of the

  20. Genomic prediction and genomic variance partitioning of daily and residual feed intake in pigs using Bayesian Power Lasso models

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, L. L. G.; Strathe, Anders Bjerring

    Improvement of feed efficiency is essential in pig breeding and selection for reduced residual feed intake (RFI) is an option. The study applied Bayesian Power LASSO (BPL) models with different power parameter to investigate genetic architecture, to predict genomic breeding values, and to partition...

  1. Predictive Ability of the Medicine Ball Chest Throw and Vertical Jump Tests for Determining Muscular Strength and Power in Adolescents

    Science.gov (United States)

    Hackett, Daniel A.; Davies, Timothy B.; Ibel, Denis; Cobley, Stephen; Sanders, Ross

    2018-01-01

    This study examined the predictive ability of the medicine ball chest throw and vertical jump for muscular strength and power in adolescents. One hundred and ninety adolescents participated in this study. Participants performed trials of the medicine ball chest throw and vertical jump, with vertical jump peak power calculated via an estimation…

  2. Nitrogen oxides emissions from thermal power plants in china: current status and future predictions.

    Science.gov (United States)

    Tian, Hezhong; Liu, Kaiyun; Hao, Jiming; Wang, Yan; Gao, Jiajia; Qiu, Peipei; Zhu, Chuanyong

    2013-10-01

    Increasing emissions of nitrogen oxides (NOx) over the Chinese mainland have been of great concern due to their adverse impacts on regional air quality and public health. To explore and obtain the temporal and spatial characteristics of NOx emissions from thermal power plants in China, a unit-based method is developed. The method assesses NOx emissions based on detailed information on unit capacity, boiler and burner patterns, feed fuel types, emission control technologies, and geographical locations. The national total NOx emissions in 2010 are estimated at 7801.6 kt, of which 5495.8 kt is released from coal-fired power plant units of considerable size between 300 and 1000 MW. The top provincial emitter is Shandong where plants are densely concentrated. The average NOx-intensity is estimated at 2.28 g/kWh, markedly higher than that of developed countries, mainly owing to the inadequate application of high-efficiency denitrification devices such as selective catalytic reduction (SCR). Future NOx emissions are predicted by applying scenario analysis, indicating that a reduction of about 40% by the year 2020 can be achieved compared with emissions in 2010. These results suggest that NOx emissions from Chinese thermal power plants could be substantially mitigated within 10 years if reasonable control measures were implemented effectively.

  3. Decentralized model predictive based load frequency control in an interconnected power system

    Energy Technology Data Exchange (ETDEWEB)

    Mohamed, T.H., E-mail: tarekhie@yahoo.co [High Institute of Energy, South Valley University (Egypt); Bevrani, H., E-mail: bevrani@ieee.or [Dept. of Electrical Engineering and Computer Science, University of Kurdistan (Iran, Islamic Republic of); Hassan, A.A., E-mail: aahsn@yahoo.co [Faculty of Engineering, Dept. of Electrical Engineering, Minia University, Minia (Egypt); Hiyama, T., E-mail: hiyama@cs.kumamoto-u.ac.j [Dept. of Electrical Engineering and Computer Science, Kumamoto University, Kumamoto (Japan)

    2011-02-15

    This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

  4. Evaluation of Haddam Neck (Connecticut Yankee) Nuclear Power Plant, environmental impact prediction, based on monitoring programs

    Energy Technology Data Exchange (ETDEWEB)

    Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Mahaffey, J.A.; Waton, D.G.

    1976-12-01

    A study was undertaken by the U.S. Nuclear Regulatory Commission (NRC) to evaluate the nonradiological environmental data obtained from three nuclear power plants operating for a period of one year or longer. The document presented reports the second of three nuclear power plants to be evaluated in detail by Battelle, Pacific Northwest Laboratories. Haddam Neck (Connecticut Yankee) Nuclear Power Plant nonradiological monitoring data were assessed to determine their effectiveness in the measurement of environmental impacts. Efforts were made to determine if: (1) monitoring programs, as designed, can detect environmental impacts, (2) appropriate statistical analyses were performed and if they were sensitive enough to detect impacts, (3) predicted impacts could be verified by monitoring programs, and (4) monitoring programs satisfied the requirements of the Environmental Technical Specifications. Both preoperational and operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the methods used to measure ecological, chemical, and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data sets were available. From this type of analysis, deficiencies in both preoperational and operational monitoring programs may be identified and provide a basis for suggested improvement.

  5. SIEX design predictions for the PNC fuel pins in the HEDL P-E01 power-to-melt test

    International Nuclear Information System (INIS)

    1979-01-01

    During the design phase of the HEDL P-E01 power-to-melt test, a series of design predictions were generated for the three PNC pins using the SIEX fuel pin modeling code. This document tabulates a series of selected PNC pin design predictions as requested by M. Shinohara during his visit to HEDL

  6. Fast Prediction of Differential Mode Noise Input Filter Requirements for FLyback and Boost Unity Power Factor Converters

    DEFF Research Database (Denmark)

    Andersen, Michael Andreas E.

    1997-01-01

    Two new and simple methods to make predictions of the differential mode (DM) input filter requirements are presented, one for flyback and one for boost unity power factor converters. They have been verified by measurements. They give the designer the ability to predict the DM input noise filter...

  7. Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

    Science.gov (United States)

    van Veen, S H C M; van Kleef, R C; van de Ven, W P M M; van Vliet, R C J A

    2018-02-01

    This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts. Copyright © 2017 John Wiley & Sons, Ltd.

  8. A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems

    Directory of Open Access Journals (Sweden)

    Yanzi Wang

    2016-01-01

    Full Text Available Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction.

  9. Power Prediction and Technoeconomic Analysis of a Solar PV Power Plant by MLP-ABC and COMFAR III, considering Cloudy Weather Conditions

    Directory of Open Access Journals (Sweden)

    M. Khademi

    2016-01-01

    Full Text Available The prediction of power generated by photovoltaic (PV panels in different climates is of great importance. The aim of this paper is to predict the output power of a 3.2 kW PV power plant using the MLP-ABC (multilayer perceptron-artificial bee colony algorithm. Experimental data (ambient temperature, solar radiation, and relative humidity was gathered at five-minute intervals from Tehran University’s PV Power Plant from September 22nd, 2012, to January 14th, 2013. Following data validation, 10665 data sets, equivalent to 35 days, were used in the analysis. The output power was predicted using the MLP-ABC algorithm with the mean absolute percentage error (MAPE, the mean bias error (MBE, and correlation coefficient (R2, of 3.7, 3.1, and 94.7%, respectively. The optimized configuration of the network consisted of two hidden layers. The first layer had four neurons and the second had two neurons. A detailed economic analysis is also presented for sunny and cloudy weather conditions using COMFAR III software. A detailed cost analysis indicated that the total investment’s payback period would be 3.83 years in sunny periods and 4.08 years in cloudy periods. The results showed that the solar PV power plant is feasible from an economic point of view in both cloudy and sunny weather conditions.

  10. Predictive maintenance: A new approach in maintenance of nuclear power plants

    International Nuclear Information System (INIS)

    Benvenuto, F.; Ferrari, L.

    2005-01-01

    The maintenance services for a Nuclear Power Plant are in general aimed at reaching the following goals: - Increase component availability and consequently decrease intervention frequency; - Reduce unexpected costs from unexpected repairs; - Progressively decrease the time of each intervention; - Improve the spare parts supply efficiency; - Improve spare parts and consumable warehouse managing; - Decrease maintenance costs. Most of the currently used maintenance activities refer to run-to-failure or preventive approaches: - Run-to-failure or Corrective Maintenance means that work is only carried out when a component or system is faulty and unable to perform its critical function. Non critical components such as filters or components with spare may be maintained in this way; - Preventive or Scheduled Maintenance involves a regular pre-set schedule programme of maintenance work. Programme outlined by the manufacturer of the component in question based on the design life of the component and based on past experience by operation. One step further than Preventive Maintenance is represented by Predictive Maintenance. Whereas Preventive Maintenance bases its schedules on past performance data, a predictive system acquires condition data from the machine to be maintained whilst the machine is in operation. The information obtained from this analysis indicates the condition in real time, provides a diagnosis of wear and shows any trend towards critical conditions. Predictive maintenance mainly consists of the following interventions: - Lubricant analysis; - Collection / analysis of functional parameters, such as motor absorption, flow rate, pressure, temperature, noise, vibration of rotating equipment, thermal efficiency, etc; - Periodical test of lifting systems; - Other operations to acquire sensitive equipment parameters. Predictive Maintenance can reduce the accidental intervention and extend the components life, and, in the end, is increasing the global availability

  11. Explicit model predictive control applications in power systems: an AGC study for an isolated industrial system

    DEFF Research Database (Denmark)

    Jiang, Hao; Lin, Jin; Song, Yonghua

    2016-01-01

    Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law...... cannot be evaluated before the MPC controller is put into service. Therefore, system operators may not validate its performances in advance. To overcome this drawback, the explicit MPC (EMPC) method is introduced and applied to obtain an explicit control law. In addition, another major contribution...... is that an improved partition algorithm of EMPC is studied which enables the EMPC method to be extended to a system of large number of state variables and more constraints. A simple single generator single load case is used to illustrate the whole procedure of EMPC and then the EMPC is applied to an actual isolated...

  12. Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

    DEFF Research Database (Denmark)

    Preindl, Matthias; Schaltz, Erik

    2010-01-01

    the use of a current controller which takes into account the discrete states of the inverter, e.g. DTC or a more modern approach: Model Predictive Direct Current Control (MPDCC). Moreover overshoots and oscillations in the speed are not desired in many applications, since they lead to mechanical stress......In drive systems the most used control structure is the cascade control with an inner torque, i.e. current and an outer speed control loop. The fairly small converter switching frequency in high power applications, e.g. wind turbines lead to modest speed control performance. An improvement bring...... behaviour. It compensates the load torque influence on the speed control setting a feed forward torque value, i.e. current reference value. The benefits are twice. The speed controller reaches immediately the speed reference value avoiding offsets which must be compensated by the weak integrator. Moreover...

  13. Technique Feature Analysis or Involvement Load Hypothesis: Estimating Their Predictive Power in Vocabulary Learning.

    Science.gov (United States)

    Gohar, Manoochehr Jafari; Rahmanian, Mahboubeh; Soleimani, Hassan

    2018-02-05

    Vocabulary learning has always been a great concern and has attracted the attention of many researchers. Among the vocabulary learning hypotheses, involvement load hypothesis and technique feature analysis have been proposed which attempt to bring some concepts like noticing, motivation, and generation into focus. In the current study, 90 high proficiency EFL students were assigned into three vocabulary tasks of sentence making, composition, and reading comprehension in order to examine the power of involvement load hypothesis and technique feature analysis frameworks in predicting vocabulary learning. It was unraveled that involvement load hypothesis cannot be a good predictor, and technique feature analysis was a good predictor in pretest to posttest score change and not in during-task activity. The implications of the results will be discussed in the light of preparing vocabulary tasks.

  14. Worldwide impact of aerosol's time scale on the predicted long-term concentrating solar power potential.

    Science.gov (United States)

    Ruiz-Arias, Jose A; Gueymard, Christian A; Santos-Alamillos, Francisco J; Pozo-Vázquez, David

    2016-08-10

    Concentrating solar technologies, which are fuelled by the direct normal component of solar irradiance (DNI), are among the most promising solar technologies. Currently, the state-of the-art methods for DNI evaluation use datasets of aerosol optical depth (AOD) with only coarse (typically monthly) temporal resolution. Using daily AOD data from both site-specific observations at ground stations as well as gridded model estimates, a methodology is developed to evaluate how the calculated long-term DNI resource is affected by using AOD data averaged over periods from 1 to 30 days. It is demonstrated here that the use of monthly representations of AOD leads to systematic underestimations of the predicted long-term DNI up to 10% in some areas with high solar resource, which may result in detrimental consequences for the bankability of concentrating solar power projects. Recommendations for the use of either daily or monthly AOD data are provided on a geographical basis.

  15. Maximal locality and predictive power in higher-dimensional, compactified field theories

    International Nuclear Information System (INIS)

    Kubo, Jisuke; Nunami, Masanori

    2004-01-01

    To realize maximal locality in a trivial field theory, we maximize the ultraviolet cutoff of the theory by fine tuning the infrared values of the parameters. This optimization procedure is applied to the scalar theory in D + 1 dimensional (D ≥ 4) with one extra dimension compactified on a circle of radius R. The optimized, infrared values of the parameters are then compared with the corresponding ones of the uncompactified theory in D dimensions, which is assumed to be the low-energy effective theory. We find that these values approximately agree with each other as long as R -1 > approx sM is satisfied, where s ≅ 10, 50, 50, 100 for D = 4,5,6,7, and M is a typical scale of the D-dimensional theory. This result supports the previously made claim that the maximization of the ultraviolet cutoff in a nonrenormalizable field theory can give the theory more predictive power. (author)

  16. Evaluation of a Trapezoidal Predictive Controller for a Four-Wire Active Power Filter for Utility Equipment of Metro Railway, Power-Land Substations

    Directory of Open Access Journals (Sweden)

    Sergio Salas-Duarte

    2016-01-01

    Full Text Available The realization of an improved predictive current controller based on a trapezoidal model is described, and the impact of this technique is assessed on the performance of a 2 kW, 21.6 kHz, four-wire, Active Power Filter for utility equipment of Metro Railway, Power-Land Substations. The operation of the trapezoidal predictive current controller is contrasted with that of a typical predictive control technique, based on a single Euler approximation, which has demonstrated generation of high-quality line currents, each using a 400 V DC link to improve the power quality of an unbalanced nonlinear load of Metro Railway. The results show that the supply current waveforms become virtually sinusoidal waves, reducing the current ripple by 50% and improving its power factor from 0.8 to 0.989 when the active filter is operated with a 1.6 kW load. The principle of operation of the trapezoidal predictive controller is analysed together with a description of its practical development, showing experimental results obtained with a 2 kW prototype.

  17. Past as Prediction: Newcomb, Huxley, The Eclipse of Thales, and The Power of Science

    Science.gov (United States)

    Stanley, Matthew

    2009-12-01

    The ancient eclipse of Thales was an important, if peculiar, focus of scientific attention in the 19th century. Victorian-era astronomers first used it as data with which to calibrate their lunar theories, but its status became strangely malleable as the century progressed. The American astronomer Simon Newcomb re-examined the eclipse and rejected it as the basis for lunar theory. But strangely, it was the unprecedented accuracy of Newcomb's calculations that led the British biologist T.H. Huxley to declare the eclipse to be the quintessential example of the power of science. Huxley argued that astronomy's ability to create "retrospective prophecy” showed how scientific reasoning was superior to religion (and incidentally, helped support Darwin's theories). Both Newcomb and Huxley declared that prediction (of past and future) was what gave science its persuasive power. The eclipse of Thales's strange journey through Victorian astronomy reveals how these two influential scientists made the case for the social and cultural authority of science.

  18. Prediction of Francis Turbine Prototype Part Load Pressure and Output Power Fluctuations with Hydroelectric Model

    Science.gov (United States)

    Alligné, S.; Nicolet, C.; Béguin, A.; Landry, C.; Gomes, J.; Avellan, F.

    2017-04-01

    The prediction of pressure and output power fluctuations amplitudes on Francis turbine prototype is a challenge for hydro-equipment industry since it is subjected to guarantees to ensure smooth and reliable operation of the hydro units. The European FP7 research project Hyperbole aims to setup a methodology to transpose the pressure fluctuations induced by the cavitation vortex rope from the reduced scale model to the prototype generating units. A Francis turbine unit of 444MW with a specific speed value of ν = 0.29, is considered as case study. A SIMSEN model of the power station including electrical system, controllers, rotating train and hydraulic system with transposed draft tube excitation sources is setup. Based on this model, a frequency analysis of the hydroelectric system is performed for all technologies to analyse potential interactions between hydraulic excitation sources and electrical components. Three technologies have been compared: the classical fixed speed configuration with Synchronous Machine (SM) and the two variable speed technologies which are Doubly Fed Induction Machine (DFIM) and Full Size Frequency Converter (FSFC).

  19. Time response prediction of Brazilian Nuclear Power Plant temperature sensors using neural networks

    International Nuclear Information System (INIS)

    Santos, Roberto Carlos dos; Pereira, Iraci Martinez

    2011-01-01

    This work presents the results of the time constants values predicted from ANN using Angra I Brazilian nuclear power plant data. The signals obtained from LCSR loop current step response test sensors installed in the process presents noise end fluctuations that are inherent of operational conditions. Angra I nuclear power plant has 20 RTDs as part of the protection reactor system. The results were compared with those obtained from traditional way. Primary coolant RTDs (Resistance Temperature Detector) typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. An in-situ test method called LCSR - loop current step response test was developed to measure remotely the response time of RTDs. In the LCSR method, the response time of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat transfer model. For this reason, this calculation is not simple and requires specialized personnel. This work combines the two methodologies, Plunge test and LCSR test, using neural networks. With the use of neural networks it will not be necessary to use the LCSR transformation to determine sensor's time constant and this leads to more robust results. (author)

  20. Predicting efficiency of solar powered hydrogen generation using photovoltaic-electrolysis devices

    Energy Technology Data Exchange (ETDEWEB)

    Gibson, Thomas L.; Kelly, Nelson A. [General Motors Research and Development Center, Chemical Science and Material Systems Laboratory, Mail Code 480-106-269, 30500 Mound Road, Warren, MI 48090-9055 (United States)

    2010-02-15

    Hydrogen fuel for fuel cell vehicles can be produced by using solar electric energy from photovoltaic (PV) modules for the electrolysis of water without emitting carbon dioxide or requiring fossil fuels. In the past, this renewable means of hydrogen production has suffered from low efficiency (2-6%), which increased the area of the PV array required and therefore, the cost of generating hydrogen. A comprehensive mathematical model was developed that can predict the efficiency of a PV-electrolyzer combination based on operating parameters including voltage, current, temperature, and gas output pressure. This model has been used to design optimized PV-electrolyzer systems with maximum solar energy to hydrogen efficiency. In this research, the electrical efficiency of the PV-electrolysis system was increased by matching the maximum power output and voltage of the photovoltaics to the operating voltage of a proton exchange membrane (PEM) electrolyzer, and optimizing the effects of electrolyzer operating current, and temperature. The operating temperature of the PV modules was also an important factor studied in this research to increase efficiency. The optimized PV-electrolysis system increased the hydrogen generation efficiency to 12.4% for a solar powered PV-PEM electrolyzer that could supply enough hydrogen to operate a fuel cell vehicle. (author)

  1. A Predictive Power Control Strategy for DFIGs Based on a Wind Energy Converter System

    Directory of Open Access Journals (Sweden)

    Xiaoliang Yang

    2017-07-01

    Full Text Available A feasible control strategy is proposed to control a doubly fed induction generator based on the wind energy converter system (DFIG-WECS. The main aim is to enhance the steady state and dynamic performance under the condition of the parameter perturbations and external disturbances and to satisfy the stator power response of the system. Within the proposed control method, the control scheme for the rotor side converter (RSC is developed on the model predictive control. Firstly, the self-adaptive reference trajectory is established from the deduced discrete state-space equation of the generator. Then, the rotor voltage is calculated by minimizing the global performance index under the current prediction steps at the sampling instant. Through the control scheme for the grid side converter (GSC and wind turbine, we have re-applied the conventional control. The effectiveness of the proposed control strategy is verified via time domain simulation of a 150 kW-575 V DFIG-WECS using Matlab/Simulink. The simulation result shows that the control of the DFIG with the proposed control method can enhance the steady and dynamic response capability better than the conventional ones when the system faces errors due to the parameter perturbations, external disturbances and the rotor speed.

  2. DR2DI: a powerful computational tool for predicting novel drug-disease associations

    Science.gov (United States)

    Lu, Lu; Yu, Hua

    2018-04-01

    Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.

  3. The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems.

    Directory of Open Access Journals (Sweden)

    Waleed Reafee

    Full Text Available The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy.

  4. The Power of Implicit Social Relation in Rating Prediction of Social Recommender Systems.

    Science.gov (United States)

    Reafee, Waleed; Salim, Naomie; Khan, Atif

    2016-01-01

    The explosive growth of social networks in recent times has presented a powerful source of information to be utilized as an extra source for assisting in the social recommendation problems. The social recommendation methods that are based on probabilistic matrix factorization improved the recommendation accuracy and partly solved the cold-start and data sparsity problems. However, these methods only exploited the explicit social relations and almost completely ignored the implicit social relations. In this article, we firstly propose an algorithm to extract the implicit relation in the undirected graphs of social networks by exploiting the link prediction techniques. Furthermore, we propose a new probabilistic matrix factorization method to alleviate the data sparsity problem through incorporating explicit friendship and implicit friendship. We evaluate our proposed approach on two real datasets, Last.Fm and Douban. The experimental results show that our method performs much better than the state-of-the-art approaches, which indicates the importance of incorporating implicit social relations in the recommendation process to address the poor prediction accuracy.

  5. Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data

    Directory of Open Access Journals (Sweden)

    Alexander P. Kartun-Giles

    2018-04-01

    Full Text Available A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing and equilibrium (static sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.

  6. A review on the young history of the wind power short-term prediction

    Energy Technology Data Exchange (ETDEWEB)

    Costa, Alexandre; Navarro, Jorge [Wind Energy, Division of Renewable Energies, Department of Energy, CIEMAT, Av. Complutense, 22, Ed. 42, 28044 Madrid (Spain); Crespo, Antonio [Laboratorio de Mecanica de Fluidos, Departmento de Ingenieria Energetica y Fluidomecanica, ETSII, Universidad Politecnica de Madrid, C/Jose Gutierrez Abascal, 2-28006 Madrid (Spain); Lizcano, Gil [Oxford University Centre for the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY (United Kingdom); Madsen, Henrik [Informatics and Mathematical Modelling - IMM, Technical University of Denmark, Richard Petersens Plads, Building 321, Office 019, 2800 Kgs. Lyngby (Denmark); Feitosa, Everaldo [Brazilian Wind Energy Centre - CBEE, Centro de Tecnologia e Geociencias, UFPE-50.740-530 Recife, PE (Brazil)

    2008-08-15

    This paper makes a brief review on 30 years of history of the wind power short-term prediction, since the first ideas and sketches on the theme to the actual state of the art on models and tools, giving emphasis to the most significant proposals and developments. The two principal lines of thought on short-term prediction (mathematical and physical) are indistinctly treated here and comparisons between models and tools are avoided, mainly because, on the one hand, a standard for a measure of performance is still not adopted and, on the other hand, it is very important that the data are exactly the same in order to compare two models (this fact makes it almost impossible to carry out a quantitative comparison between a huge number of models and methods). In place of a quantitative description, a qualitative approach is preferred for this review, remarking the contribution (and innovative aspect) of each model. On the basis of the review, some topics for future research are pointed out. (author)

  7. Comparison of the observed and predicted visual effects caused by power plant plumes

    Science.gov (United States)

    Bergstrom, Robert W.; Seigneur, Christian; Babson, Betsy L.; Holman, Hoi-Ying; Wojcik, Michael A.

    One of the objectives of the June-July and December 1979 E.P.A.-VISTTA field programs was to obtain the data necessary to evaluate the components of the EPA/SAI plume visibility model. The data were obtained through a coordinated set of measurements of specific power plant emissions, meteorological conditions, plume concentrations measured by an aircraft and through telephotometer measurements of the visual effects of the plume. This paper presents a comparison of measurements obtained between 4 and 32 km downwind from the plant with model predictions. The various components of the plume visibility model were evaluated independently and it was found that the greatest uncertainties in the model predictions are in the diffusion module, which has the limitations associated with all Gaussian diffusion models. The chemistry module describes plume chemistry well in a clean background atmosphere; however, the rate of NO-to-NO 2 conversion is slightly overpredicted in the model. Predicted secondary aerosol formation is negligible. The optics module of the visibility model was evaluated by predicting the optical effects of the plume on the basis of the airplane-measured plume concentrations. These calculations were compared with the ground-based telephotometer measurements. The optics module tends to slightly overestimate the plume visual effect; the average absolute relative errors in measurements of the plume/sky intensity ratio are 6.0, 6.4, 3.0 and 4.8% at 405, 450, 550 and 630 nm, respectively. For contrast values below - 0.06, the contrast predicted by the optics module is within a factor of 2 of the measured values. A major uncertainty in the data is found in the degree of alignment between the airplane and telephotometer measurements. The overall evaluation of the plume visibility model was carried out with 20 sets of measurements. It was found that the model tends to overestimate the visual effect of the plume. The average absolute relative errors in the plume

  8. Numerical Predictions of Wind Turbine Power and Aerodynamic Loads for the NREL Phase II and IV Combined Experiment Rotor

    Science.gov (United States)

    Duque, Earl P. N.; Johnson, Wayne; vanDam, C. P.; Chao, David D.; Cortes, Regina; Yee, Karen

    1999-01-01

    Accurate, reliable and robust numerical predictions of wind turbine rotor power remain a challenge to the wind energy industry. The literature reports various methods that compare predictions to experiments. The methods vary from Blade Element Momentum Theory (BEM), Vortex Lattice (VL), to variants of Reynolds-averaged Navier-Stokes (RaNS). The BEM and VL methods consistently show discrepancies in predicting rotor power at higher wind speeds mainly due to inadequacies with inboard stall and stall delay models. The RaNS methodologies show promise in predicting blade stall. However, inaccurate rotor vortex wake convection, boundary layer turbulence modeling and grid resolution has limited their accuracy. In addition, the inherently unsteady stalled flow conditions become computationally expensive for even the best endowed research labs. Although numerical power predictions have been compared to experiment. The availability of good wind turbine data sufficient for code validation experimental data that has been extracted from the IEA Annex XIV download site for the NREL Combined Experiment phase II and phase IV rotor. In addition, the comparisons will show data that has been further reduced into steady wind and zero yaw conditions suitable for comparisons to "steady wind" rotor power predictions. In summary, the paper will present and discuss the capabilities and limitations of the three numerical methods and make available a database of experimental data suitable to help other numerical methods practitioners validate their own work.

  9. Predictive Power of Primary and Secondary School Success Criterion on Transition to Higher Education Examination Scores

    Directory of Open Access Journals (Sweden)

    Atilla ÖZDEMİR

    2016-12-01

    Full Text Available It is seen that education has a significant effect that changes an individual’s life in our country in which education is a way of moving up the social ladder. In order to continue to a higher education program after graduating from high school, students have to succeed in transition to higher education examination. Thus, the entrance exam is an important factor to determine the future of the students. In our country, middle school grades and high school grade point average that is added to university placement score are also determinants. When spiral structure of our curriculum is considered, it is expected that related courses’ grades at middle school will predict the scores obtained from the first stage of transition to higher education exam (YGS. Since high school grade point average forms university placement score, being aware of how related courses’ achievement scores at secondary school predict raw scores of YGS subtests is significant in terms of our education system’s feedback and integrity. As a result, observing students’ achievement scores in related courses during their middle and high school education longitudinally and predicting raw scores on the subtests of the first stage of university entrance exam, YGS, from middle school and high scool achievement scores are substantial with regards to provide feedback to our education system. Because of those reasons, the predictive power of 7th - 12th grade year-end grade point averages ofstudents who took YGS in 2013 on their 2013 YGS subtests’ raw scvores is examined. Students who took YGS exam in Ankara province at 2012-2013 school year formed the aimed population of this study and 533 students who took YGS exam in 2013 in Altındağ district of Ankara formed target population of the study. Data was obtained from 533 students at three different schools in Altındağ district of Ankara province. Stepwise multiple regression analysis was used to answer research questions

  10. Wind Power Forecasting Using Multi-Objective Evolutionary Algorithms for Wavelet Neural Network-Optimized Prediction Intervals

    Directory of Open Access Journals (Sweden)

    Yanxia Shen

    2018-01-01

    Full Text Available The intermittency of renewable energy will increase the uncertainty of the power system, so it is necessary to predict the short-term wind power, after which the electrical power system can operate reliably and safely. Unlike the traditional point forecasting, the purpose of this study is to quantify the potential uncertainties of wind power and to construct prediction intervals (PIs and prediction models using wavelet neural network (WNN. Lower upper bound estimation (LUBE of the PIs is achieved by minimizing a multi-objective function covering both interval width and coverage probabilities. Considering the influence of the points out of the PIs to shorten the width of PIs without compromising coverage probability, a new, improved, multi-objective artificial bee colony (MOABC algorithm combining multi-objective evolutionary knowledge, called EKMOABC, is proposed for the optimization of the forecasting model. In this paper, some comparative simulations are carried out and the results show that the proposed model and algorithm can achieve higher quality PIs for wind power forecasting. Taking into account the intermittency of renewable energy, such a type of wind power forecast can actually provide a more reliable reference for dispatching of the power system.

  11. The power within: The experimental manipulation of power interacts with trait BDD symptoms to predict interoceptive accuracy.

    Science.gov (United States)

    Kunstman, Jonathan W; Clerkin, Elise M; Palmer, Kateyln; Peters, M Taylar; Dodd, Dorian R; Smith, April R

    2016-03-01

    This study tested whether relatively low levels of interoceptive accuracy (IAcc) are associated with body dysmorphic disorder (BDD) symptoms. Additionally, given research indicating that power attunes individuals to their internal states, we sought to determine if state interoceptive accuracy could be improved through an experimental manipulation of power.. Undergraduate women (N = 101) completed a baseline measure of interoceptive accuracy and then were randomized to a power or control condition. Participants were primed with power or a neutral control topic and then completed a post-manipulation measure of state IAcc. Trait BDD symptoms were assessed with a self-report measure. Controlling for baseline IAcc, within the control condition, there was a significant inverse relationship between trait BDD symptoms and interoceptive accuracy. Continuing to control for baseline IAcc, within the power condition, there was not a significant relationship between trait BDD symptoms and IAcc, suggesting that power may have attenuated this relationship. At high levels of BDD symptomology, there was also a significant simple effect of experimental condition, such that participants in the power (vs. control) condition had better interoceptive accuracy. These results provide initial evidence that power may positively impact interoceptive accuracy among those with high levels of BDD symptoms.. This cross-sectional study utilized a demographically homogenous sample of women that reflected a broad range of symptoms; thus, although there were a number of participants reporting elevated BDD symptoms, these findings might not generalize to other populations or clinical samples. This study provides the first direct test of the relationship between trait BDD symptoms and IAcc, and provides preliminary evidence that among those with severe BDD symptoms, power may help connect individuals with their internal states. Future research testing the mechanisms linking BDD symptoms with IAcc, as

  12. Generate networks with power-law and exponential-law distributed degrees: with applications in link prediction of tumor pathways

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-03-01

    Full Text Available In present study I proposed a method for generating biological networks based on power-law (p(x=x^(-a and exponential-law (p(x=e^(-ax distribution functions. Given the parameter of power-law or exponential-law distribution function, a, the algorithm generates an expected frequency distribution according to the given parameter, thereafter creates an adjacency matrix in which (practical frequency distribution of node degrees matches the expected frequency distribution. The results showed that power-law distribution function performs much better than exponential-law distribution function in generating networks. Using the revised algorithm, tumor related networks (pathways are simulated and predicted. The results prove that the algorithm is overall effective in predicting network links (14.6%-21.2%of correctly predicted links against 0.1%-3.4% of that for random assignments. Matlab codes of the algorithms are given also.

  13. A neural network based computational model to predict the output power of different types of photovoltaic cells.

    Directory of Open Access Journals (Sweden)

    WenBo Xiao

    Full Text Available In this article, we introduced an artificial neural network (ANN based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-, multi-crystalline (multi-, and amorphous (amor- crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

  14. Different Predictive Control Strategies for Active Load Management in Distributed Power Systems with High Penetration of Renewable Energy Sources

    DEFF Research Database (Denmark)

    Zong, Yi; Bindner, Henrik W.; Gehrke, Oliver

    2013-01-01

    strategies are able to achieve load shifting and enable end users to participate in market-based power systems, and thus profit from optimal consumption of energy in relation to price and supply of ancillary services in the power system, as well as improve grids with integration of high penetration......, greenhouses, etc.), and to coordinate the management of large numbers of distributed energy resources with the smart grid solution. This paper presents different predictive control (Genetic Algorithm-based and Model Predictive Control-based) strategies that schedule controlled loads in the industrial...... and residential sectors, based on dynamic power price and weather forecast, considering users’ comfort settings to meet an optimization objective, such as maximum profit or minimum energy consumption. Some field tests were carried out on a facility for intelligent, active and distributed power systems, which...

  15. Nuclear power can reduce emissions and maintain a strong economy: Rating Australia’s optimal future electricity-generation mix by technologies and policies

    International Nuclear Information System (INIS)

    Hong, Sanghyun; Bradshaw, Corey J.A.; Brook, Barry W.

    2014-01-01

    Highlights: • Nuclear power is essential for reducing greenhouse-gas emissions at lower cost. • Physical and economic limits of renewables at high penetrations hamper their growth. • Large-scale fossil fuels are required if nuclear power is not permitted in Australia. • Well-balanced information is a prerequisite for defining an optimal future mix. - Abstract: Legal barriers currently prohibit nuclear power for electricity generation in Australia. For this reason, published future electricity scenarios aimed at policy makers for this country have not seriously considered a full mix of energy options. Here we addressed this deficiency by comparing the life-cycle sustainability of published scenarios using multi-criteria decision-making analysis, and modeling the optimized future electricity mix using a genetic algorithm. The published ‘CSIRO e-future’ scenario under its default condition (excluding nuclear) has the largest aggregate negative environmental and economic outcomes (score = 4.51 out of 8), followed by the Australian Energy Market Operator’s 100% renewable energy scenario (4.16) and the Greenpeace scenario (3.97). The e-future projection with maximum nuclear-power penetration allowed yields the lowest negative impacts (1.46). After modeling possible future electricity mixes including or excluding nuclear power, the weighted criteria recommended an optimized scenario mix where nuclear power generated >40% of total electricity. The life-cycle greenhouse-gas emissions of the optimization scenarios including nuclear power were <27 kg CO 2 -e MW h −1 in 2050, which achieves the IPCC’s target of 50–150 kg CO 2 -e MW h −1 . Our analyses demonstrate clearly that nuclear power is an effective and logical option for the environmental and economic sustainability of a future electricity network in Australia

  16. Predicting speech intelligibility based on the signal-to-noise envelope power ratio after modulation-frequency selective processing

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    A model for predicting the intelligibility of processed noisy speech is proposed. The speech-based envelope power spectrum model has a similar structure as the model of Ewert and Dau [(2000). J. Acoust. Soc. Am. 108, 1181-1196], developed to account for modulation detection and masking data....... The model estimates the speech-to-noise envelope power ratio, SNR env, at the output of a modulation filterbank and relates this metric to speech intelligibility using the concept of an ideal observer. Predictions were compared to data on the intelligibility of speech presented in stationary speech...... process provides a key measure of speech intelligibility. © 2011 Acoustical Society of America....

  17. Energy Supply Characteristics of a Combined Solar Cell and Diesel Engine System with a Prediction Algorithm for Solar Power Generation

    Science.gov (United States)

    El-Sayed, Abeer Galal; Obara, Shin'ya

    The production of electricity from the solar cells continues to attract interest as a power source for distributed energy generation. It is important to be able to estimate solar cell power to optimize system energy management. This paper proposes a prediction algorithm based on a neural network (NN) to predict the electricity production from a solar cell. The operation plan for a combined solar cell and diesel engine generator system is examined using the NN prediction algorithm. Two systems are examined in this paper: one with and one without a power storage facility. Comparisons are presented of the results from the two systems with respect to the actual calculations of output power and the predicted electricity production from the solar cell. The exhaust heat from the engine is used to supply the heat demand. A back-up boiler is operated when the engine exhaust heat is insufficient to meet the heat demand. Electricity and heat are supplied to the demand side from the proposed systems, and no external sources are used. When the NN production-of-electricity prediction was introduced, the engine generator operating time was reduced by 12.5% in December and 16.7% for March and September. Moreover, an operation plan for the combined system exhaust heat is proposed, and the heat output characteristics of the back-up boiler are characterized.

  18. Modeling and Predicting the EUR/USD Exchange Rate: The Role of Nonlinear Adjustments to Purchasing Power Parity

    OpenAIRE

    Jesús Crespo Cuaresma; Anna Orthofer

    2010-01-01

    Reliable medium-term forecasts are essential for forward-looking monetary policy decisionmaking. Traditionally, predictions of the exchange rate tend to be linked to the equilibrium concept implied by the purchasing power parity (PPP) theory. In particular, the traditional benchmark for exchange rate models is based on a linear adjustment of the exchange rate to the level implied by PPP. In the presence of aggregation effects, transaction costs or uncertainty, however, economic theory predict...

  19. Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Geon Pil; Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun, E-mail: magyna@chosun.ac.kr

    2016-04-15

    Highlights: • We present a hydrogen-concentration prediction method in an NPP containment. • The cascaded fuzzy neural network (CFNN) is used in this prediction model. • The CFNN model is much better than the existing FNN model. • This prediction can help prevent severe accidents in NPP due to hydrogen explosion. - Abstract: Recently, severe accidents in nuclear power plants (NPPs) have attracted worldwide interest since the Fukushima accident. If the hydrogen concentration in an NPP containment is increased above 4% in atmospheric pressure, hydrogen combustion will likely occur. Therefore, the hydrogen concentration must be kept below 4%. This study presents the prediction of hydrogen concentration using cascaded fuzzy neural network (CFNN). The CFNN model repeatedly applies FNN modules that are serially connected. The CFNN model was developed using data on severe accidents in NPPs. The data were obtained by numerically simulating the accident scenarios using the MAAP4 code for optimized power reactor 1000 (OPR1000) because real severe accident data cannot be obtained from actual NPP accidents. The root-mean-square error level predicted by the CFNN model is below approximately 5%. It was confirmed that the CFNN model could accurately predict the hydrogen concentration in the containment. If NPP operators can predict the hydrogen concentration in the containment using the CFNN model, this prediction can assist them in preventing a hydrogen explosion.

  20. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks

    International Nuclear Information System (INIS)

    Zameer, Aneela; Arshad, Junaid; Khan, Asifullah; Raja, Muhammad Asif Zahoor

    2017-01-01

    Highlights: • Genetic programming based ensemble of neural networks is employed for short term wind power prediction. • Proposed predictor shows resilience against abrupt changes in weather. • Genetic programming evolves nonlinear mapping between meteorological measures and wind-power. • Proposed approach gives mathematical expressions of wind power to its independent variables. • Proposed model shows relatively accurate and steady wind-power prediction performance. - Abstract: The inherent instability of wind power production leads to critical problems for smooth power generation from wind turbines, which then requires an accurate forecast of wind power. In this study, an effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming. In contrast to existing series based combination of wind power predictors, whereby the error or variation in the leading predictor is propagated down the stream to the next predictors, the proposed intelligent ensemble predictor avoids this shortcoming by introducing Genetical Programming based semi-stochastic combination of neural networks. It is observed that the decision of the individual base regressors may vary due to the frequent and inherent fluctuations in the atmospheric conditions and thus meteorological properties. The novelty of the reported work lies in creating ensemble to generate an intelligent, collective and robust decision space and thereby avoiding large errors due to the sensitivity of the individual wind predictors. The proposed ensemble based regressor, Genetic Programming based ensemble of Artificial Neural Networks, has been implemented and tested on data taken from five different wind farms located in Europe. Obtained numerical results of the proposed model in terms of various error measures are compared with the recent artificial intelligence based strategies to demonstrate the

  1. The role of predictive on-line monitoring systems in the power generation industry

    Energy Technology Data Exchange (ETDEWEB)

    Gittings, S.D.; Baldwin, J. [AEA Technology Energy plc (United Kingdom)

    1998-12-31

    It has been apparent in the power generation sector for some time that utilities are moving away from large scale, labour intensive, inspection and overhaul programmes. These are being replaced by targeted inspection and replacement programmes supported by engineering assessment. Such engineering assessments address the predominant long-term damage mechanisms and are aimed at predicting failure timescales based upon historic operating data. From these predictions extended inspection intervals can be justified and replacement schedules planned in advance, ensuring maximum plant availability and deferred capital expenditure. However, as these assessments are based upon an examination of past operating history they must be periodically re-visited and up-dated. The cost of such reassessments is usually close to that performed initially, although cost savings can arise from reductions in work-scope which have been previously justified. As computers have become more advanced (and significantly cheaper) some of the expertise used in these assessments has been transferred into software based products. However, these products are generally aimed at replacing specific parts of the desk-based analysis, necessitating a suite of products to be used in order to address all of the components and damage mechanisms, which must be assessed. As these products require specialized knowledge to be used effectively they are often employed by consultants and rarely by plant operators, except in the largest of organisations which can support an in-house team of `experts`. This has in essence led to an increase in the number of companies capable of offering assessment services, but has maintained, in the majority of cases, the plant operators reliance upon external consultants. These software based assessment methods rely upon historic operating data (in the same manner as their desk- based counterparts) and hence also need to be periodically up-dated. However, as previous assessments can

  2. Towards more accurate wind and solar power prediction by improving NWP model physics

    Science.gov (United States)

    Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

    2014-05-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

  3. Intraocular Lens Power Calculation after Refractive Surgery: A Comparative Analysis of Accuracy and Predictability

    OpenAIRE

    Kang, Byeong Soo; Han, Jeong Mo; Oh, Joo Youn; Kim, Mee Kum; Wee, Won Ryang

    2017-01-01

    Purpose To compare the accuracy of intraocular lens (IOL) power calculation using conventional regression formulae or the American Society of Cataract and Refractive Surgery (ASCRS) IOL power calculator for previous corneal refractive surgery. Methods We retrospectively reviewed 96 eyes from 68 patients that had undergone cataract surgery after keratorefractive surgeries. We calculated the formula with two approaches: IOL powers using the ASCRS IOL power calculator and IOL powers using conven...

  4. Discussion of “Prediction intervals for short-term wind farm generation forecasts” and “Combined nonparametric prediction intervals for wind power generation”

    DEFF Research Database (Denmark)

    Pinson, Pierre; Tastu, Julija

    2014-01-01

    A new score for the evaluation of interval forecasts, the so-called coverage width-based criterion (CWC), was proposed and utilized.. This score has been used for the tuning (in-sample) and genuine evaluation (out-ofsample) of prediction intervals for various applications, e.g., electric load [1......], electricity prices [2], general purpose prediction [3], and wind power generation [4], [5]. Indeed, two papers by the same authors appearing in the IEEE Transactions On Sustainable Energy employ that score and use it to conclude on the comparative quality of alternative approaches to interval forecasting...

  5. Experience and benefits from using the EPRI MOV Performance Prediction Methodology in nuclear power plants

    International Nuclear Information System (INIS)

    Walker, T.; Damerell, P.S.

    1999-01-01

    The EPRI MOV Performance Prediction Methodology (PPM) is an effective tool for evaluating design basis thrust and torque requirements for MOVs. Use of the PPM has become more widespread in US nuclear power plants as they close out their Generic Letter (GL) 89-10 programs and address MOV periodic verification per GL 96-05. The PPM has also been used at plants outside the US, many of which are implementing programs similar to US plants' GL 89-10 programs. The USNRC Safety Evaluation of the PPM and the USNRC's discussion of the PPM in GL 96-05 make the PPM an attractive alternative to differential pressure (DP) testing, which can be costly and time-consuming. Significant experience and benefits, which are summarized in this paper, have been gained using the PPM. Although use of PPM requires a commitment of resources, the benefits of a solidly justified approach and a reduced need for DP testing provide a substantial safety and economic benefit. (author)

  6. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts

    NARCIS (Netherlands)

    F. Ravazzolo (Francesco); C. Zhou (Chen); C. Huurman

    2007-01-01

    textabstractIn the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been

  7. Three dimensional numerical prediction of icing related power and energy losses on a wind turbine

    Science.gov (United States)

    Sagol, Ece

    Regions of Canada experience harsh winter conditions that may persist for several months. Consequently, wind turbines located in these regions are exposed to ice accretion and its adverse effects, from loss of power to ceasing to function altogether. Since the weather-related annual energy production loss of a turbine may be as high as 16% of the nominal production for Canada, estimating these losses before the construction of a wind farm is essential for investors. A literature survey shows that most icing prediction methods and codes are developed for aircraft, and, as this information is mostly considered corporate intellectual property, it is not accessible to researchers in other domains. Moreover, aircraft icing is quite different from wind turbine icing. Wind turbines are exposed to icing conditions for much longer periods than aircraft, perhaps for several days in a harsh climate, whereas the maximum length of exposure of an aircraft is about 3-4 hours. In addition, wind turbine blades operate at subsonic speeds, at lower Reynolds numbers than aircraft, and their physical characteristics are different. A few icing codes have been developed for wind turbine icing nevertheless. However, they are either in 2D, which does not consider the 3D characteristics of the flow field, or they focus on simulating each rotation in a time-dependent manner, which is not practical for computing long hours of ice accretion. Our objective in this thesis is to develop a 3D numerical methodology to predict rime ice shape and the power loss of a wind turbine as a function of wind farm icing conditions. In addition, we compute the Annual Energy Production of a sample turbine under both clean and icing conditions. The sample turbine we have selected is the NREL Phase VI experimental wind turbine installed on a wind farm in Sweden, the icing events at which have been recorded and published. The proposed method is based on computing and validating the clean performance of the turbine

  8. The application of a high pulse repetition rate CO2 laser with high average power for isotope separation by molecular dissociation in a strong IR field

    International Nuclear Information System (INIS)

    Bagratashvili, V.N.; Kolomisky, Y.R.; Letokhov, V.S.; Ryabov, E.A.; Baranov, V.Y.; Kazakov, S.A.; Nizjev, V.G.; Pismenny, V.D.; Starodubtsev, A.I.; Velikhov, E.P.

    1977-01-01

    Considering a SF 6 molecule we demonstrate feasibility of using high pulse repetition rate CO 2 laser for isotope separation by selective molecular dissociation in a strong IR field. Dependences of dissociation efficiency as well as separation selectivity on pulse repetition rate up to 150 Hz are investigated. The inherent thermal effects are discussed. (orig.) [de

  9. Combined prediction model for supply risk in nuclear power equipment manufacturing industry based on support vector machine and decision tree

    International Nuclear Information System (INIS)

    Shi Chunsheng; Meng Dapeng

    2011-01-01

    The prediction index for supply risk is developed based on the factor identifying of nuclear equipment manufacturing industry. The supply risk prediction model is established with the method of support vector machine and decision tree, based on the investigation on 3 important nuclear power equipment manufacturing enterprises and 60 suppliers. Final case study demonstrates that the combination model is better than the single prediction model, and demonstrates the feasibility and reliability of this model, which provides a method to evaluate the suppliers and measure the supply risk. (authors)

  10. Influence of a strong laser field on Coulomb explosion and stopping power of energetic H{sub 3}{sup +} clusters in plasmas

    Energy Technology Data Exchange (ETDEWEB)

    Wang Guiqiu; Gao Hong; Wang Yaochuan; Yao Li; Zhong Haiyang; Cheng Lihong; Yang Kun; Liu Wei [Department of Physics, Dalian Maritime University, Dalian 116026 (China); E Peng; Xu Dianguo [Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001 (China); Wang Younian; Hu Zhanghu [School of Physics and Optoelectronic Technology, Dalian University of Technology, Dalian 116023 (China)

    2012-09-15

    The influence of a high-intensity laser field on the Coulomb explosion and stopping power for a swift H{sub 3}{sup +} cluster ion in a plasma target is studied by means of the molecular dynamic (MD) method based on the linearized Vlasov-Poisson theory. Excitations of the plasma are described by the classical plasma dielectric function. In the presence of the laser field, the general expressions for the induced potential in the target and the interaction force among the ions within the cluster are derived. Based on the numerical solution of the equations of motion for the constituent ions, the Coulomb explosion patterns and the cluster's stopping power are discussed for a range of laser parameters. Numerical results show that the laser field affects the correlation between the ions and contributes to weaken the wake effect and the stopping power as compared to the laser-free case. On the other hand, the stopping power ratio of H{sub 3}{sup +} cluster is higher than the situation of dicluster of H{sub 2}{sup +} due to the vicinage effect in the cluster.

  11. The state-of-the-art in short-term prediction of wind power. A literature overview

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Brownsword, R.; Kariniotakis, G.

    2003-08-01

    Based on an appropriate questionnaire (WP1.1) and some other works already in progress, this report details the state-of-the-art in short term prediction of wind power, mostly summarising nearly all existing literature on the topic. (au)

  12. Regression and tracing methodology based prediction of oncoming demand and losses in deregulated operation of power systems

    DEFF Research Database (Denmark)

    Nallagownden, P.; Mukerjee, R.N.; Masri, S.

    2010-01-01

    scenario, a capability to predict the stated inputs in advance, are desirable. Regression and Proportional sharing based power tracing method using linear equations, determines different transactions to supply a specific retailer's demand and the losses related to each transaction. The learning...

  13. Predictive Power of Prospective Physical Education Teachers' Attitudes towards Educational Technologies for Their Technological Pedagogical Content Knowledge

    Science.gov (United States)

    Varol, Yaprak Kalemoglu

    2015-01-01

    The aim of the research is to determine the predictive power of prospective physical education teachers' attitudes towards educational technologies for their technological pedagogical content knowledge. In this study, a relational research model was used on a study group that consisted of 529 (M[subscript age]=21.49, SD=1.44) prospective physical…

  14. Analysis of Academic Self-Efficacy, Self-Esteem and Coping with Stress Skills Predictive Power on Academic Procrastination

    Science.gov (United States)

    Kandemir, Mehmet; Ilhan, Tahsin; Ozpolat, Ahmed Ragip; Palanci, Mehmet

    2014-01-01

    The goal of this research is to analyze the predictive power level of academic self-efficacy, self-esteem and coping with stress on academic procrastination behavior. Relational screening model is used in the research whose research group is made of 374 students in Kirikkale University, Education Faculty in Turkey. Students in the research group…

  15. The Predictive Power of Fifth Graders' Learning Styles on Their Mathematical Reasoning and Spatial Ability

    Science.gov (United States)

    Danisman, Sahin; Erginer, Ergin

    2017-01-01

    The purpose of this study was to examine fifth graders' mathematical reasoning and spatial ability, to identify a correlation with their learning styles, and to determine the predictive power of their learning styles on their mathematical learning profiles. This causal study was conducted with 97 fifth graders (60 females, 61.9% and 37 males,…

  16. Predicted versus actual intraocular lens power in silicon-oil-filled eyes undergoing cataract extraction using automated intraoperative retinoscopy.

    Science.gov (United States)

    Elbendary, Amal M; Elwan, Mohamed M

    2012-08-01

    To compare predicted intraocular lens (IOL) power obtained with adjusted ultrasound biometry versus actual power obtained with automated intraoperative retinoscopy (AIR) in eyes undergoing combined cataract extraction and silicon oil removal in the same session. Fifty eyes with significant cataract; requiring silicon removal were included. Preoperative ultrasonic biometry with adjusted velocity (980 m/s) was recorded. After silicon removal, AIR was done and IOL power was calculated and inserted. Postoperative refraction was recorded up to 3 months. AIR was successfully obtained in all eyes. Significant correlation (p = 0.000, R = 0.91) was detected between mean power of predicted (15.8 ± 8.4) and implanted IOL (11.7 ± 8.5). Mean postoperative refraction was +0.53 ± 0.31 at 1 week, +0.40 ± 0.35 at 1 month and +0.12 ± 0.20 at 3 months. The difference was statistically significant in all time intervals. Myopic shift occurred in 37% of eyes at the third month. AIR in combined cataract extraction and silicon oil removal is easy and provides predictable outcome in all eyes. It represents a bypass to all methods of biometry based on axial length measurement. Future correction formula based on adjusted ultrasound velocity can be a simple alternative and predictable method.

  17. Prediction on Power Produced from Power Turbine as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Safarudin Gazali Herawan

    2016-01-01

    Full Text Available The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM in a sedan car. The amount of heat energy from exhaust is presented and the experimental test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. However, the existence of WHRM affects the performance of engine by slightly reducing the power. The simulation method is created using an artificial neural network (ANN which predicts the power produced from the WHRM.

  18. Benefits for wind energy in electricity markets from using short term wind power prediction tools: a simulation study

    International Nuclear Information System (INIS)

    Usaola, J.; Ravelo, O.; Gonzalez, G.; Soto, F.; Davila, M.C.; Diaz-Guerra, B.

    2004-01-01

    One of the characteristics of wind energy, from the grid point of view, is its non-dispatchability, i.e. generation cannot be ordered, hence integration in electrical networks may be difficult. Short-term wind power prediction-tools could make this integration easier, either by their use by the grid System Operator, or by promoting the participation of wind farms in the electricity markets and using prediction tools to make their bids in the market. In this paper, the importance of a short-term wind power-prediction tool for the participation of wind energy systems in electricity markets is studied. Simulations, according to the current Spanish market rules, have been performed to the production of different wind farms, with different degrees of accuracy in the prediction tool. It may be concluded that income from participation in electricity markets is increased using a short-term wind power prediction-tool of average accuracy. This both marginally increases income and also reduces the impact on system operation with the improved forecasts. (author)

  19. Detection of a Low Power Communication Signal in the Presence of a Strong Co-Channel TV Broadcast Interference Using a Kalman Filter

    Science.gov (United States)

    2014-12-01

    Television Systems Committee AWGN additive white Gaussian noise BCH Bose-Chaudhuri-Hocquengham bps bits per second CIR channel impulse response... impulse responses (CIRs) are used both for equalization in the respective DVB-T2 receivers and in the KF to estimate the state of the DVB-T2 signal...if a communication system can operate reliably in the presence of a strong, co-channel interference, then there is no need to buy spectrum, an

  20. Using Rényi parameter to improve the predictive power of singular value decomposition entropy on stock market

    Science.gov (United States)

    Jiang, Jiaqi; Gu, Rongbao

    2016-04-01

    This paper generalizes the method of traditional singular value decomposition entropy by incorporating orders q of Rényi entropy. We analyze the predictive power of the entropy based on trajectory matrix using Shanghai Composite Index and Dow Jones Index data in both static test and dynamic test. In the static test on SCI, results of global granger causality tests all turn out to be significant regardless of orders selected. But this entropy fails to show much predictability in American stock market. In the dynamic test, we find that the predictive power can be significantly improved in SCI by our generalized method but not in DJI. This suggests that noises and errors affect SCI more frequently than DJI. In the end, results obtained using different length of sliding window also corroborate this finding.

  1. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.

    2011-01-01

    The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear...... Prediction System (EPS) can be used as indicator of a three-hourly, three days ahead, wind power forecast’s accuracy. In particular it has been noticed that to extract usable information from data the Ensemble members needed to be statistically calibrated, since the rank histograms for the three-day period...... that a reliable estimation of their uncertainty could be a useful information too. In fact the prediction accuracy is unfortunately not constant and can depend on the location of a particular wind farm, on the forecast time and on the atmospheric situation. Previous studies indicated that the ECMWF Ensemble...

  2. Generation of strong electromagnetic power at 35 GHz from the interaction of a resonant cavity with a relativistic electron beam generated by a free electron laser

    International Nuclear Information System (INIS)

    Lefevre, Thibaut

    2000-01-01

    The next generation of electron-positron linear colliders must reach the TeV energy range. For this, one requires an adequate RF power source to feed the accelerating cavities of the collider. One way to generate this source is to use the Two Beam Accelerator concept in which the RF power is produced in resonant cavities driven by an intense bunched beam. In this thesis, I present the experimental results obtained at the CEA/CESTA using an electron beam generated by an induction linac. First, some studies were performed with the LELIA induction linac (2.2 MeV, 1 kA, 80 ns) using a Free Electron Laser (FEL) as a buncher at 35 GHz. A second part relates the experiment made with the PIVAIR induction linac (7 MeV, 1 kA, 80 ns) in order to measure the RF power extracted from a resonant cavity at 35 GHz, which is driven by the bunches produced in the FEL. One can also find a simple theoretical modeling of the beam-cavity interaction, and the numerical results dealing with the design of the cavity we tested. (author) [fr

  3. Spontaneous Alpha Power Lateralization Predicts Detection Performance in an Un-Cued Signal Detection Task.

    Directory of Open Access Journals (Sweden)

    Gonzalo Boncompte

    Full Text Available Focusing one's attention by external guiding stimuli towards a specific area of the visual field produces systematical neural signatures. One of the most robust is the change in topological distribution of oscillatory alpha band activity across parieto-occipital cortices. In particular, decreases in alpha activity over contralateral and/or increases over ipsilateral scalp sites, respect to the side of the visual field where attention was focused. This evidence comes mainly from experiments where an explicit cue informs subjects where to focus their attention, thus facilitating detection of an upcoming target stimulus. However, recent theoretical models of attention have highlighted a stochastic or non-deterministic component related to visuospatial attentional allocation. In an attempt to evidence this component, here we analyzed alpha activity in a signal detection paradigm in the lack of informative cues; in the absence of preceding information about the location (and time of appearance of target stimuli. We believe that the unpredictability of this situation could be beneficial for unveiling this component. Interestingly, although total alpha power did not differ between Seen and Unseen conditions, we found a significant lateralization of alpha activity over parieto-occipital electrodes, which predicted behavioral performance. This effect had a smaller magnitude compared to paradigms in which attention is externally guided (cued. However we believe that further characterization of this spontaneous component of attention is of great importance in the study of visuospatial attentional dynamics. These results support the presence of a spontaneous component of visuospatial attentional allocation and they advance pre-stimulus alpha-band lateralization as one of its neural signatures.

  4. Power

    DEFF Research Database (Denmark)

    Elmholdt, Claus Westergård; Fogsgaard, Morten

    2016-01-01

    In this chapter, we will explore the dynamics of power in processes of creativity, and show its paradoxical nature as both a bridge and a barrier to creativity in organisations. Recent social psychological experimental research (Slighte, de Dreu & Nijstad, 2011) on the relation between power...... and floating source for empowering people in the organisation. We will explore and discuss here the potentials, challenges and pitfalls of power in relation to creativity in the life of organisations today. The aim is to demonstrate that power struggles may be utilised as constructive sources of creativity...

  5. Investigation of Predictive Power of Mathematics Anxiety on Mathematics Achievement in Terms of Gender and Class Variables

    Directory of Open Access Journals (Sweden)

    Mustafa İLHAN

    2013-12-01

    Full Text Available This research aims to explore predictive power of mathematics anxiety in terms of gender and class variables. For this purpose relational model was used in the study. Working group of the research consists of 348 secondary school second stage students, 175 of whom are girls and 175 are boys, having education in four elementary schools in central district of Diyarbakır province, during 2011-2012 Academic Year, first Semester. “Math Anxiety Scale for Primary School Students” to determine students’ mathematics anxiety was used. Averages of students’ mathematics notes in the first term of 2011- 2012 academic year are taken as the achievement scores of mathematics. The collected data has been analyzed by SPSS 17.0. The relationship between mathematics achievement and math anxiety was analyzed with pearson correlation. The predictor power of math anxiety for mathematics achievement was determined by the regression analysis. According the research findings %17 of the total variance of mathematics achievement can be explained by math anxiety. It has been determined that predictive power of mathematics anxiety on mathematics success is higher in girls than boys. Furthermore, it has been determined in the research that predictive power of mathematics anxiety on mathematics success increases, as students proceed towards the next grade.

  6. Simulations research of the global predictive control with self-adaptive in the gas turbine of the nuclear power plant

    International Nuclear Information System (INIS)

    Su Jie; Xia Guoqing; Zhang Wei

    2007-01-01

    For further improving the dynamic control capabilities of the gas turbine of the nuclear power plant, this paper puts forward to apply the algorithm of global predictive control with self-adaptive in the rotate speed control of the gas turbine, including control structure and the design of controller in the base of expounding the math model of the gas turbine of the nuclear power plant. the simulation results show that the respond of the change of the gas turbine speed under the control algorithm of global predictive control with self-adaptive is ten second faster than that under the PID control algorithm, and the output value of the gas turbine speed under the PID control algorithm is 1%-2% higher than that under the control slgorithm of global predictive control with self-adaptive. It shows that the algorithm of global predictive control with self-adaptive can better control the output of the speed of the gas turbine of the nuclear power plant and get the better control effect. (authors)

  7. The assessment of different models to predict solar module temperature, output power and efficiency for Nis, Serbia

    International Nuclear Information System (INIS)

    Pantic, Lana S.; Pavlović, Tomislav M.; Milosavljević, Dragana D.; Radonjic, Ivana S.; Radovic, Miodrag K.; Sazhko, Galina

    2016-01-01

    Five different models for calculating solar module temperature, output power and efficiency for sunny days with different solar radiation intensities and ambient temperatures are assessed in this paper. Thereafter, modeled values are compared to the experimentally obtained values for the horizontal solar module in Nis, Serbia. The criterion for determining the best model was based on the statistical analysis and the agreement between the calculated and the experimental values. The calculated values of solar module temperature are in good agreement with the experimentally obtained ones, with some variations over and under the measured values. The best agreement between calculated and experimentally obtained values was for summer months with high solar radiation intensity. The nonlinear model for calculating the output power is much better than the linear model and at the same time predicts better the total electrical energy generated by the solar module during the day. The nonlinear model for calculating the solar module efficiency predicts the efficiency higher than the STC (Standard Test Conditions) value of solar module efficiency for all conditions, while the linear model predicts the solar module efficiency very well. This paper provides a simple and efficient guideline to estimate relevant parameters of a monocrystalline silicon solar module under the moderate-continental climate conditions. - Highlights: • Linear model for solar module temperature gives accurate predictions for August. • The nonlinear model better predicts the solar module power than the linear model. • For calculating solar module power for Nis we propose the nonlinear model. • For calculating solar model efficiency for Nis we propose adoption of linear model. • The adopted models can be used for calculations throughout the year.

  8. Model predictions of the results of interferometric observations for stars under conditions of strong gravitational scattering by black holes and wormholes

    International Nuclear Information System (INIS)

    Shatskiy, A. A.; Kovalev, Yu. Yu.; Novikov, I. D.

    2015-01-01

    The characteristic and distinctive features of the visibility amplitude of interferometric observations for compact objects like stars in the immediate vicinity of the central black hole in our Galaxy are considered. These features are associated with the specifics of strong gravitational scattering of point sources by black holes, wormholes, or black-white holes. The revealed features will help to determine the most important topological characteristics of the central object in our Galaxy: whether this object possesses the properties of only a black hole or also has characteristics unique to wormholes or black-white holes. These studies can be used to interpret the results of optical, infrared, and radio interferometric observations

  9. Model predictions of the results of interferometric observations for stars under conditions of strong gravitational scattering by black holes and wormholes

    Energy Technology Data Exchange (ETDEWEB)

    Shatskiy, A. A., E-mail: shatskiy@asc.rssi.ru; Kovalev, Yu. Yu.; Novikov, I. D. [Russian Academy of Sciences, Astro Space Center, Lebedev Physical Institute (Russian Federation)

    2015-05-15

    The characteristic and distinctive features of the visibility amplitude of interferometric observations for compact objects like stars in the immediate vicinity of the central black hole in our Galaxy are considered. These features are associated with the specifics of strong gravitational scattering of point sources by black holes, wormholes, or black-white holes. The revealed features will help to determine the most important topological characteristics of the central object in our Galaxy: whether this object possesses the properties of only a black hole or also has characteristics unique to wormholes or black-white holes. These studies can be used to interpret the results of optical, infrared, and radio interferometric observations.

  10. Assessment of CPPF Reduction After Adapting Our Own Power Distribution Prediction Method

    International Nuclear Information System (INIS)

    Kim, Sung-min; Park, Joong-woo; Kho, Dae-hack; Seo, Hyung-beom; Han, Bong-gyun

    2006-01-01

    CANDU reactor which uses natural Uranium as a fuel requires daily on-power refueling due to its insufficient excess reactivity. So its power distribution changes unceasingly. Reactor physicists identify the change twice a week using RFSP (Reactor Fuelling Simulation Program) code, and check if it is operated within power limitation (380 channels and 4560 bundles). We also calculate ROP (Regional Overpower Protection trip system) detector calibration target value (DC, Detector Calibration) and determine refueling channels and their order. DC is composed of CPPF (Channel Power Peaking Factor), Plant ageing penalty and the factor considering PHT (Primary Heat Transport system) condition, etc. The lower CPPF means the lower DC, and the lower DC means the larger operating margin. If the reactor is not operated in full power state as Wolsong Unit no. 1, the additional operating margin can be converted to the additional operating power directly

  11. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

    Directory of Open Access Journals (Sweden)

    Xiang-ming Gao

    2017-01-01

    Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  12. Development of prediction models for radioactive caesium distribution within the 80-km radius of the Fukushima Daiichi nuclear power plant

    International Nuclear Information System (INIS)

    Kinase, Sakae; Sato, Satoshi; Saito, Kimiaki; Takahashi, Tomoyuki; Sakamoto, Ryuichi

    2014-01-01

    Preliminary prediction models have been studied for the radioactive caesium distribution within the 80-km radius of the Fukushima Daiichi nuclear power plant. The models were represented by exponential functions using ecological half-life of radioactive caesium in the environment. The ecological half-lives were derived from the changes in ambient dose equivalent rates through vehicle-borne surveys. It was found that the ecological half-lives of radioactive caesium were not constant within the 80-km radius of the Fukushima Daiichi nuclear power plant. The ecological half-life of radioactive caesium in forest areas was found to be much larger than that in urban and water areas. (authors)

  13. Long-term predictions of ambient dose equivalent rates after the Fukushima Daiichi nuclear power plant accident

    International Nuclear Information System (INIS)

    Kinase, Sakae; Saito, Kimiaki; Takahashi, Tomoyuki

    2017-01-01

    To analyze radiation protection strategies and rehabilitation programs in Fukushima, prediction models have been developed for ambient dose equivalent rate distributions within the 80 km-radius around the Fukushima Daiichi nuclear power plant. The prediction models characterized by ecological half-lives of radioactive caesium for land-use, enable Fukushima residents to obtain distribution maps of ambient dose equivalent rates after the Fukushima Daiichi nuclear power plant accident. Model parameters such as the ecological half-lives for the short-term component and the fractional distribution of short-term component were evaluated using ambient dose equivalent rates through car/vehicle-borne surveys. It was found that the ecological half-lives among land-use differ only slightly, whereas the fractional distributions of the short-term component are clearly dependent on land-use. In addition, uncertainties concerning predictions of ambient dose equivalent rates arising from variability in model parameters were assessed using Monte Carlo simulations. Long-term changes of ambient dose equivalent rates were predicted for different land-use areas. Distribution maps of ambient dose equivalent rates for the next 30 years after the accident, created by the prediction models are expected to be useful for follow-up of the radiological situation since they provide information on the space variation of the ambient dose equivalent rates in inhabited areas. (author)

  14. Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant

    International Nuclear Information System (INIS)

    Wang, Hang; Peng, Min-jun; Wu, Peng; Cheng, Shou-yu

    2016-01-01

    Highlights: • Different methods for online monitoring and diagnosis are summarized. • Numerical simulation modeling of condensate and feed water system in nuclear power plant are done by FORTRAN programming. • Integrated online monitoring and prediction methods have been developed and tested. • Online monitoring module, fault diagnosis module and trends prediction module can be verified with each other. - Abstract: Faults or accidents may occur in a nuclear power plant (NPP), but it is hard for operators to recognize the situation and take effective measures quickly. So, online monitoring, diagnosis and prediction (OMDP) is used to provide enough information to operators and improve the safety of NPPs. In this paper, distributed conservation equation (DCE) and artificial immunity system (AIS) are proposed for online monitoring and diagnosis. On this basis, quantitative simulation models and interactive database are combined to predict the trends and severity of faults. The effectiveness of OMDP in improving the monitoring and prediction of condensate and feed water system (CFWS) was verified through simulation tests.

  15. Evaluating the predictive power of multivariate tensor-based morphometry in Alzheimer's disease progression via convex fused sparse group Lasso

    Science.gov (United States)

    Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

    2014-03-01

    Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

  16. Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso.

    Science.gov (United States)

    Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

    2014-03-21

    Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method 1 with novel MR-based multivariate morphometric surface map of the hippocampus 2 to predict future cognitive scores of patients. Previous work by Zhou et al. 1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al. 2 s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

  17. Improving predictive power of physically based rainfall-induced shallow landslide models: a probablistic approach

    Science.gov (United States)

    Raia, S.; Alvioli, M.; Rossi, M.; Baum, R.L.; Godt, J.W.; Guzzetti, F.

    2013-01-01

    are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, i.e. the Frontignano (Italy) and the Mukilteo (USA) areas, respectively. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the Message Passing Interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

  18. Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach

    Directory of Open Access Journals (Sweden)

    S. Raia

    2014-03-01

    of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, that is, the Frontignano (Italy and the Mukilteo (USA areas. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the message passing interface (MPI on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

  19. The predictive power of depression screening procedures for veterans with coronary artery disease

    Directory of Open Access Journals (Sweden)

    Shankman SA

    2012-04-01

    Full Text Available Stewart A Shankman1*, Jeffrey Nadelson2*, Sarah Kate McGowan1, Ali A Sovari2, Mladen I Vidovich21Department of Psychiatry and Psychology, University of Illinois, 2Department of Cardiology, Jesse Brown VA Medical Center, Chicago, IL, USA*These authors contributed equally to this workAbstract: Depression leads to a worse outcome for patients with coronary artery disease (CAD. Thus, accurately identifying depression in CAD patients is imperative. In many veterans affairs (VA hospitals, patients are screened for depression once a year using the patient health questionnaire (PHQ-9. Although the PHQ-9 is generally considered a specific and sensitive measure of depression, there is reason to believe that these screening procedures may miss a large number of cases of depression within CAD patients and cardiology patients more generally. The goal of this study was to provide data as to the predictive power of this depression screening procedure by (a comparing the prevalence rate of depression identified by the PHQ-9 to known prevalence rates and (b examining whether patients identified as “depressed” also had conditions that consistently co-occur with depression (eg, post-traumatic stress disorder [PTSD], other medical issues. Participants were 813 consecutive patients who received an angiogram in the cardiac catheterization laboratory at a large VA Medical Center. Prevalence of depression was 6.9% in the overall sample and less than 6% when the sample was restricted to CAD patients with significant stenosis. Depression was significantly associated with PTSD, smoking, and alcohol problems. However, depression was not associated with other medical problems such as diabetes, renal failure, peripheral vascular disease, or anemia. In conclusion, the low prevalence rate of depression and lack of associations with comorbid medical problems may suggest that the VA’s depression screening procedures have low sensitivity for identifying depression in CAD

  20. Adult Scandinavians' use of powered scooters: user satisfaction, frequency of use, and prediction of daily use.

    Science.gov (United States)

    Sund, Terje; Brandt, Åse

    2018-04-01

    To investigate user satisfaction with characteristics of powered scooters (scooters), frequency of use, and factors predicting daily scooter use. Cross-sectional. Adult scooter users (n = 59) in Denmark and Norway, mean age 74.5 (standard deviation 12.3) years. Structured face-to-face interviews. The NOMO 1.0, the Quebec User Evaluation of Satisfaction with assistive devices (QUEST 2.0), and a study specific instrument were used to collect data. Descriptive statistics were applied, and regression analyzes were used to investigate predictors for daily scooter use. The International Classification of Functioning, Disability and Health (ICF) served as a framework for classifying variables and guiding the investigation. Satisfaction with the scooter characteristics was high with most participants being very satisfied or quite satisfied (66.1-91.5%). Most scooters were used daily (36.2%) or several times a week (50.0%). User satisfaction with safety of the scooter [odds ratio (OR) = 11.76, confidence interval (CI) = 1.70-81.28] and reduced balance (OR = 5.63, CI = 0.90-35.39) increased the likelihood of daily use, while reduced function in back and/or legs (OR = .04, CI = 0.00-0.75), tiredness (OR = .06, CI = 0.01-0.51), and increased age (OR = .93, CI = 0.87-1.00) reduced the likelihood of daily use. 52.8% of the variance was explained by these variables. User satisfaction was high, and most scooters were used frequently. User satisfaction with safety, specific functional limitations and age were predictors for daily scooter use. Implications for Rehabilitation Scooters seem to be a beneficial intervention for people with mobility impairment: user satisfaction and frequency of use are high. Users' subjective feeling of safety should be secured in the service delivery process in order to support safe and frequent scooter use. Training of scooter skills should be considered in the service delivery process.

  1. A feasibility study of a predictive emissions monitoring system applied to taipower's nanpu and hsinta power plants.

    Science.gov (United States)

    Chien, Tsung-Wen; Hsueh, Hsin-Ta; Chu, Hsin; Hsu, Wei-Chieh; Tu, Yueh-Yuan; Tsai, Hsien-Shiou; Chen, Kuo-Yi

    2010-08-01

    The Hsinta and Nanpu Power Stations are located in southern Taiwan. The Hsinta Power Station consists of five combined-cycle gas turbines (CCGT), whereas the Nanpu Power Station consists of four. A project was undertaken to develop and deploy a predictive emissions monitoring system (PEMS) on CCGT unit 3 of Hsinta Power Station (HT-3) and CCGT unit 1 of Nanpu Power Station (NP-1) with the long-term goal of developing a universal model for this kind of power plant. After the first-year PEMS project at the Hsinta power plant, one goal of the second-year PEMS project was to set up a second PEMS at the Nanpu power plant and compare the PEM models applied the to two gas-fired combined cycle power generation units. Consequently, the second and third PEMS of Taiwan at CCGT HT-3 and NP-1 were finished. After comparing the differences among HT-1, HT-3, and NP-1 PEMS models, the pattern of model functionality indicated that this model could be applied to the other units of the same type and size. However, the PEMS function constant or parameter coefficients must be modified on a case-by-case basis. With regard to the PEMS model developed for HT-3, the relative accuracy (RA) of the 15-variable model with start-up mode is only 7.43% and met the criteria of draft PS-16. With regard to the PEMS model developed for NP-1, the RA of the 10-variable model with start-up mode was only 7.76% and also met the criteria of draft PS-16.

  2. Supervisory hybrid model predictive control for voltage stability of power networks

    NARCIS (Netherlands)

    Negenborn, R.R.; Beccuti, A.G.; Demiray, T.; Leirens, S.; Damm, G.; De Schutter, B.; Morari, M.

    2007-01-01

    Emergency voltage control problems in electric power networks have stimulated the interest for the implementation of online optimal control techniques. Briefly stated, voltage instability stems from the attempt of load dynamics to restore power consumption beyond the capability of the transmission

  3. Model predictive control for power flows in networks with limited capacity

    DEFF Research Database (Denmark)

    Biegel, Benjamin; Stoustrup, Jakob; Bendtsen, Jan Dimon

    2012-01-01

    We consider an interconnected network of consumers powered through an electrical grid of limited capacity. A subset of the consumers are intelligent consumers and have the ability to store energy in a controllable fashion; they can be filled and emptied as desired under power and capacity...... ensuring high performance....

  4. Adaptation and Implementation of Predictive Maintenance Technique with Nondestructive Testing for Power Plants

    International Nuclear Information System (INIS)

    Jung, Gye Jo; Jung, Nam Gun

    2010-01-01

    Many forces are pressuring utilities to reduce operating and maintenance costs without cutting back on reliability or availability. Many utility managers are re-evaluating maintenance strategies to meet these demands. To utilities how to reduce maintenance costs and extent the effective operating life of equipment, predictive maintenance technique can be adapted. Predictive maintenance had three types program which are in-house program, engineering company program and mixed program. We can approach successful predictive maintenance program with 'smart trust' concept

  5. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  6. The SURA Coastal Ocean Observing and Prediction (SCOOP) Program: Adapting Web 2.0 technologies to power next generation science

    Science.gov (United States)

    Bogden, P.; Partners, S.

    2008-12-01

    The Web 2.0 has helped globalize the economy and change social interactions, but the full impact on coastal sciences has yet to be realized. The SCOOP program (www.OpenIOOS.org/about/sura.html), an initiative of the Coastal Research Committee of the Southeastern Universities Research Association (SURA), has been using Web 2.0 technologies to create infrastructure for a multi-disciplinary Distributed Coastal Laboratory (DCL). In the spirit of the Web 2.0, SCOOP strives to provide an open-access virtual facility where "virtual visiting" scientists can log in, perform experiments (e.g., evaluate new wetting/drying algorithms in several different inundation models), potentially contribute to the assembly of resources (e.g., leave their algorithms for others), and then move on. The SCOOP prototype has focused on storm surge and waves (the initial science focus), and integrates a real-time data network to evaluate the predictions. The multi-purpose SCOOP components support a sensor-web initiative (www.OOSTethys.org) that is co-led by SURA. SCOOP also includes portals with real-time visualization, workflow configuration and decision-tool prototypes (www.OpenIOOS.org), powered by distributed computing resources from multiple universities across the nation (www.sura.org/SURAgrid). Based on our experience, we propose three key ingredients for initiatives to have the biggest impact on coastal science: (1) standards, (2) working prototypes and (3) communities of interest. We strongly endorse the Open Geospatial Consortium - a geospatial analog of the World Wide Web consortium - and other international consensus-standards bodies that engage government, private sector and academic involvement. But these standards are often highly complex, which can be an impediment to their use. We have overcome such hurdles with the second key ingredient: a focused working prototype. The prototype should include guides and resources that make it easy for others to apply, test, and revise the

  7. MODEL PREDICTIVE CONTROL FOR PHOTOVOLTAIC STATION MAXIMUM POWER POINT TRACKING SYSTEM

    Directory of Open Access Journals (Sweden)

    I. Elzein

    2015-01-01

    Full Text Available The purpose of this paper is to present an alternative maximum power point tracking, MPPT, algorithm for a photovoltaic module, PVM, to produce the maximum power, Pmax, using the optimal duty ratio, D, for different types of converters and load matching.We present a state-based approach to the design of the maximum power point tracker for a stand-alone photovoltaic power generation system. The system under consideration consists of a solar array with nonlinear time-varying characteristics, a step-up converter with appropriate filter.The proposed algorithm has the advantages of maximizing the efficiency of the power utilization, can be integrated to other MPPT algorithms without affecting the PVM performance, is excellent for Real-Time applications and is a robust analytical method, different from the traditional MPPT algorithms which are more based on trial and error, or comparisons between present and past states. The procedure to calculate the optimal duty ratio for a buck, boost and buck-boost converters, to transfer the maximum power from a PVM to a load, is presented in the paper. Additionally, the existence and uniqueness of optimal internal impedance, to transfer the maximum power from a photovoltaic module using load matching, is proved.

  8. A global perspective on renewable energy resources. NASA's prediction of worldwide energy resources (power) project

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Taiping; Chandler, William S.; Hoell, James M.; Westberg, David; Whitlock, Charles H. [SSAI/NASA Langley Research Center, Hampton, VA (United States); Stackhouse, Paul W. Jr [NASA Langley Research Center, Hampton, VA (United States)

    2008-07-01

    The Prediction of the Worldwide Energy Resources (POWER) Project, initiated under the NASA Science Mission Directorate Applied Science Energy Management Program, analyzes, synthesizes and makes available data parameters on a global scale. These data have proved to be reliable and useful to the renewable energy industries, especially to the solar energy sectors. The POWER project derives its data primarily from NASA's World Climate Research Programme (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Version 2.9) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (Version 4). The latest development of the NASA POWER Project and its plans for the future are presented in this paper. (orig.)

  9. Optimal Active Power Control of A Wind Farm Equipped with Energy Storage System based on Distributed Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

    2016-01-01

    This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm...... is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads...... to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger wind turbine load reduction, compared to the conventional wind farm control without ESS. Moreover, the efficiency of the developed D-MPC algorithm...

  10. IBM SPSS modeler essentials effective techniques for building powerful data mining and predictive analytics solutions

    CERN Document Server

    McCormick, Keith; Wei, Bowen

    2017-01-01

    IBM SPSS Modeler allows quick, efficient predictive analytics and insight building from your data, and is a popularly used data mining tool. This book will guide you through the data mining process, and presents relevant statistical methods which are used to build predictive models and conduct other analytic tasks using IBM SPSS Modeler. From ...

  11. Ibm spss modeler essentials effective techniques for building powerful data mining and predictive analytics solutions

    CERN Document Server

    McCormick, Keith; Wei, Bowen

    2017-01-01

    IBM SPSS Modeler allows quick, efficient predictive analytics and insight building from your data, and is a popularly used data mining tool. This book will guide you through the data mining process, and presents relevant statistical methods which are used to build predictive models and conduct other analytic tasks using IBM SPSS Modeler. From ...

  12. NOx, Soot, and Fuel Consumption Predictions under Transient Operating Cycle for Common Rail High Power Density Diesel Engines

    Directory of Open Access Journals (Sweden)

    N. H. Walke

    2016-01-01

    Full Text Available Diesel engine is presently facing the challenge of controlling NOx and soot emissions on transient cycles, to meet stricter emission norms and to control emissions during field operations. Development of a simulation tool for NOx and soot emissions prediction on transient operating cycles has become the most important objective, which can significantly reduce the experimentation time and cost required for tuning these emissions. Hence, in this work, a 0D comprehensive predictive model has been formulated with selection and coupling of appropriate combustion and emissions models to engine cycle models. Selected combustion and emissions models are further modified to improve their prediction accuracy in the full operating zone. Responses of the combustion and emissions models have been validated for load and “start of injection” changes. Model predicted transient fuel consumption, air handling system parameters, and NOx and soot emissions are in good agreement with measured data on a turbocharged high power density common rail engine for the “nonroad transient cycle” (NRTC. It can be concluded that 0D models can be used for prediction of transient emissions on modern engines. How the formulated approach can also be extended to transient emissions prediction for other applications and fuels is also discussed.

  13. Experimental and Numerical Simulations Predictions Comparison of Power and Efficiency in Hydraulic Turbine

    Directory of Open Access Journals (Sweden)

    Laura Castro

    2011-01-01

    Full Text Available On-site power and mass flow rate measurements were conducted in a hydroelectric power plant (Mexico. Mass flow rate was obtained using Gibson's water hammer-based method. A numerical counterpart was carried out by using the commercial CFD software, and flow simulations were performed to principal components of a hydraulic turbine: runner and draft tube. Inlet boundary conditions for the runner were obtained from a previous simulation conducted in the spiral case. The computed results at the runner's outlet were used to conduct the subsequent draft tube simulation. The numerical results from the runner's flow simulation provided data to compute the torque and the turbine's power. Power-versus-efficiency curves were built, and very good agreement was found between experimental and numerical data.

  14. Energy-neutral solar-powered street lighting with predictive and adaptive behaviour

    OpenAIRE

    Lau, Sei Ping; Weddell, Alex S.; Merrett, Geoff V.; White, NM

    2014-01-01

    Street lighting can enhance the safety and security of residential and commercial areas. However, its installation and operation is expensive: cables must be installed, and power is drawn from the grid which is typically dominated by non-renewable sources. A potential solution is the use of solar energy to power individual street lights locally. However, with limited energy storage and variable solar availability, existing lighting control strategies are unsuitable for this application. This ...

  15. Experimental and Numerical Simulations Predictions Comparison of Power and Efficiency in Hydraulic Turbine

    OpenAIRE

    Castro, Laura; Urquiza, Gustavo; Adamkowski, Adam; Reggio, Marcelo

    2011-01-01

    On-site power and mass flow rate measurements were conducted in a hydroelectric power plant (Mexico). Mass flow rate was obtained using Gibson's water hammer-based method. A numerical counterpart was carried out by using the commercial CFD software, and flow simulations were performed to principal components of a hydraulic turbine: runner and draft tube. Inlet boundary conditions for the runner were obtained from a previous simulation conducted in the spiral case. The computed results at the ...

  16. Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation

    Directory of Open Access Journals (Sweden)

    Dirk Cannon

    2017-06-01

    Full Text Available State-of-the-art wind power forecasts beyond a few hours ahead rely on global numerical weather prediction models to forecast the future large-scale atmospheric state. Often they provide initial and boundary conditions for nested high resolution simulations. In this paper, both upper and lower bounds on forecast range are identified within which global ensemble forecasts provide skilful information for system-wide wind power applications. An upper bound on forecast range is associated with the limit of predictability, beyond which forecasts have no more skill than predictions based on climatological statistics. A lower bound is defined at the lead time beyond which the resolved uncertainty associated with estimating the future large-scale atmospheric state is larger than the unresolved uncertainty associated with estimating the system-wide wind power response to a given large-scale state.The bounds of skilful ensemble forecast range are quantified for three leading global forecast systems. The power system of Great Britain (GB is used as an example because independent verifying data is available from National Grid. The upper bound defined by forecasts of GB-total wind power generation at a specific point in time is found to be 6–8 days. The lower bound is found to be 1.4–2.4 days. Both bounds depend on the global forecast system and vary seasonally. In addition, forecasts of the probability of an extreme power ramp event were found to possess a shorter limit of predictability (4.5–5.5 days. The upper bound on this forecast range can only be extended by improving the global forecast system (outside the control of most users or by changing the metric used in the probability forecast. Improved downscaling and microscale modelling of the wind farm response may act to decrease the lower bound. The potential gain from such improvements have diminishing returns beyond the short-range (out to around 2 days.

  17. Automated system for load flow prediction in power substations using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Arlys Michel Lastre Aleaga

    2015-09-01

    Full Text Available The load flow is of great importance in assisting the process of decision making and planning of generation, distribution and transmission of electricity. Ignorance of the values in this indicator, as well as their inappropriate prediction, difficult decision making and efficiency of the electricity service, and can cause undesirable situations such as; the on demand, overheating of the components that make up a substation, and incorrect planning processes electricity generation and distribution. Given the need for prediction of flow of electric charge of the substations in Ecuador this research proposes the concept for the development of an automated prediction system employing the use of Artificial Neural Networks.

  18. Domestic Refrigerators Temperature Prediction Strategy for the Evaluation of the Expected Power Consumption

    DEFF Research Database (Denmark)

    Lakshmanan, Venkatachalam; Marinelli, Mattia; Kosek, Anna Magdalena

    2013-01-01

    This paper discusses and presents a simple temperature prediction strategy for the domestic refrigerator. The main idea is to predict the duration it takes to the Cold chamber temperature to reach the thresholds according to the state of the compressor and to the last temperature measurements....... The experiments are conducted at SYSLAB facility at DTU Risø Campus having a set of refrigerators working at different set point temperatures, with different ambient temperatures and under different thermal load conditions. The prediction strategy is tested using a set of different refrigerators in order...

  19. Does IQ predict total and cardiovascular disease mortality as strongly as other risk factors? Comparison of effect estimates using the Vietnam Experience Study.

    Science.gov (United States)

    Batty, G D; Shipley, M J; Gale, C R; Mortensen, L H; Deary, I J

    2008-12-01

    To compare the strength of the relation of two measurements of IQ and 11 established risk factors with total and cardiovascular disease (CVD) mortality. Cohort study of 4166 US male former army personnel with data on IQ test scores (in early adulthood and middle age), a range of established risk factors and 15-year mortality surveillance. When CVD mortality (n = 61) was the outcome of interest, the relative index of inequality (RII: hazard ratio; 95% CI) for the most disadvantaged relative to the advantaged (in descending order of magnitude of the first six based on age-adjusted analyses) was: 6.58 (2.54 to 17.1) for family income; 5.55 (2.16 to 14.2) for total cholesterol; 5.12 (2.01 to 13.0) for body mass index; 4.70 (1.89 to 11.7) for IQ in middle age; 4.29 (1.70 to 10.8) for blood glucose and 4.08 (1.63 to 10.2) for high-density lipoprotein cholesterol (the RII for IQ in early adulthood was ranked tenth: 2.88; 1.19 to 6.97). In analyses featuring all deaths (n = 233), the RII for risk factors most strongly related to this outcome was 7.46 (4.54 to 12.3) for family income; 4.41 (2.77 to 7.03) for IQ in middle age; 4.02 (2.37 to 6.83) for smoking; 3.81 (2.35 to 6.17) for educational attainment; 3.40 (2.14 to 5.41) for pulse rate and 3.26 (2.06 to 5.15) for IQ in early adulthood. Multivariable adjustment led to marked attenuation of these relations, particularly those for IQ. Lower scores on measures of IQ at two time points were associated with CVD and, particularly, total mortality, at a level of magnitude greater than several other established risk factors.

  20. The predictive power of personality traits on insomnia symptoms: a longitudinal study among shift workers

    OpenAIRE

    Larsgård, Borgar

    2015-01-01

    Shift work can have adverse effects on employees' health, including symptoms of insomnia. This may cause severe problems both for employee and employer. The personality variables morningness, neuroticism and extraversion, along with some demographic variables (e.g. gender, age) have been found to correlate with insomnia symptoms, but predictive data have been scarce. This study sought to discover whether personality variables could predict insomnia. A hierarchical longitudinal (six months)...

  1. A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

    Directory of Open Access Journals (Sweden)

    Marino Marrocu

    2017-11-01

    Full Text Available An accurate forecast of the power generated by a wind turbine is of paramount importance for its optimal exploitation. Several forecasting methods have been proposed either based on a physical modeling or using a statistical approach. All of them rely on the availability of high quality measures of local wind speed, corresponding generated power and on numerical weather forecasts. In this paper, a simple and effective wind power forecast technique, based on the probability distribution mapping of wind speed forecast and observed power data, is presented and it is applied to two turbines located on the island of Borkum (Germany in the North Sea. The wind speed forecast of the ECMWF model at 100 m from the ground is used as the prognostic meteorological parameter. Training procedures are based entirely on relatively short time series of power measurements. Results show that our approach has skills that are similar or better than those obtained using more standard methods when measured with mean absolute error.

  2. A prediction of Power Duration Curve from the Optimal Operation of the Multi Reservoirs System

    Directory of Open Access Journals (Sweden)

    Abdul Wahab Younis

    2013-04-01

    Full Text Available  This study aims of predication Power Duration Curves(PDC resulting from the optimal operation of the multi reservoirs system which comprises the reservoirs of Bakhma dam,Dokan dam and Makhool dam for the division of years over 30 years.Discrete Differential Dynamic Programming(DDDP has been employed to find the optimal operation of the said reservoirs.    PDC representing the relationship between the generated hydroelectric power and percentage of operation time equaled or exceeded . The importance of these curves lies in knowing the volume of electric power available for that percentage of operation time. The results have shown that the sum of yearly hydroelectric power for average Release and for the single operation was 5410,1604,2929(Mwfor the reservoirs of Bakhma, Dokan, Makhool dams, which resulted from the application of independent DDDP technology. Also, the hydroelectric power whose generation can be guranteed for 90% of the time is 344.91,107.7,188.15 (Mw for the single operation and 309.1,134.08,140.7 (Mw for the operation as a one system for the reservoirs of Bakhma, Dokan, and Makhool dams respectively.

  3. The prediction of the impact of climatic factors on short-term electric power load based on the big data of smart city

    Science.gov (United States)

    Qiu, Yunfei; Li, Xizhong; Zheng, Wei; Hu, Qinghe; Wei, Zhanmeng; Yue, Yaqin

    2017-08-01

    The climate changes have great impact on the residents’ electricity consumption, so the study on the impact of climatic factors on electric power load is of significance. In this paper, the effects of the data of temperature, rainfall and wind of smart city on short-term power load is studied to predict power load. The authors studied the relation between power load and daily temperature, rainfall and wind in the 31 days of January of one year. In the research, the authors used the Matlab neural network toolbox to establish the combinational forecasting model. The authors trained the original input data continuously to get the internal rules inside the data and used the rules to predict the daily power load in the next January. The prediction method relies on the accuracy of weather forecasting. If the weather forecasting is different from the actual weather, we need to correct the climatic factors to ensure accurate prediction.

  4. Ideal MHD Stability Prediction and Required Power for EAST Advanced Scenario

    International Nuclear Information System (INIS)

    Chen Junjie; Li Guoqiang; Qian Jinping; Liu Zixi

    2012-01-01

    The Experimental Advanced Superconducting Tokamak (EAST) is the first fully superconducting tokamak with a D-shaped cross-sectional plasma presently in operation. The ideal magnetohydrodynamic (MHD) stability and required power for the EAST advanced tokamak (AT) scenario with negative central shear and double transport barrier (DTB) are investigated. With the equilibrium code TOQ and stability code GATO, the ideal MHD stability is analyzed. It is shown that a moderate ratio of edge transport barriers' (ETB) height to internal transport barriers' (ITBs) height is beneficial to ideal MHD stability. The normalized beta β N limit is about 2.20 (without wall) and 3.70 (with ideal wall). With the scaling law of energy confinement time, the required heating power for EAST AT scenario is calculated. The total heating power P t increases as the toroidal magnetic field B T or the normalized beta β N is increased. (magnetically confined plasma)

  5. Ideal MHD Stability Prediction and Required Power for EAST Advanced Scenario

    Science.gov (United States)

    Chen, Junjie; Li, Guoqiang; Qian, Jinping; Liu, Zixi

    2012-11-01

    The Experimental Advanced Superconducting Tokamak (EAST) is the first fully superconducting tokamak with a D-shaped cross-sectional plasma presently in operation. The ideal magnetohydrodynamic (MHD) stability and required power for the EAST advanced tokamak (AT) scenario with negative central shear and double transport barrier (DTB) are investigated. With the equilibrium code TOQ and stability code GATO, the ideal MHD stability is analyzed. It is shown that a moderate ratio of edge transport barriers' (ETB) height to internal transport barriers' (ITBs) height is beneficial to ideal MHD stability. The normalized beta βN limit is about 2.20 (without wall) and 3.70 (with ideal wall). With the scaling law of energy confinement time, the required heating power for EAST AT scenario is calculated. The total heating power Pt increases as the toroidal magnetic field BT or the normalized beta βN is increased.

  6. Automated lake-wide erosion predictions and economic damage calculations upstream of the Moses-Saunders power dam

    International Nuclear Information System (INIS)

    Zuzek, P.; Baird, W.F.; International Joint Commission, Ottawa, ON

    2008-01-01

    This presentation discussed an automated flood and erosion prediction system designed for the upstream sections of the Moses-Saunders power dam. The system included a wave prediction component along with 3-D maps, hourly run-ups, geographic information system (GIS) tools and a hazard analysis tool. Parcel, reach, township, and county databases were used to populate the system. The prediction system was used to develop detailed study sites of shore units in the study area. Shoreline classes included sand and cohesive buffs, low banks, coarse beaches, and cobble or boulder lags. Time series plots for Lake Ontario water and wave levels were presented. Great Lakes ice cover data were also included in the system as well as erosion predictions from 1961 to 1995. The system was also used to develop bluff recession equations and cumulative recession analyses for different regulation plans. Cumulative bluff recession and protection requirements were outlined. Screenshots of the flood and erosion prediction system interface were also included. tabs., figs

  7. Testing the predictive power of cognitive atypicalities in autistic children: evidence from a 3-year follow-up study.

    Science.gov (United States)

    Pellicano, Elizabeth

    2013-08-01

    This follow-up study investigated the predictive power of early cognitive atypicalities. Specifically, it examined whether early individual differences in specific cognitive skills, including theory of mind, executive function, and central coherence, could uniquely account for variation in autistic children's behaviors-social communication, repetitive behaviors, and interests and insistence on sameness-at follow-up. Thirty-seven cognitively able children with an autism spectrum condition were assessed on tests tapping verbal and nonverbal ability, theory of mind (false-belief prediction), executive function (planning ability, cognitive flexibility, and inhibitory control), and central coherence (local processing) at intake and their behavioral functioning (social communication, repetitive behaviors and interests, insistence on sameness) 3 years later. Individual differences in early executive but not theory of mind skills predicted variation in children's social communication. Individual differences in children's early executive function also predicted the degree of repetitive behaviors and interests at follow-up. There were no predictive relationships between early central coherence and children's insistence on sameness. These findings challenge the notion that distinct cognitive atypicalities map on to specific behavioral features of autism. Instead, early variation in executive function plays a key role in helping to shape autistic children's emerging behaviors, including their social communication and repetitive behaviors and interests. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

  8. Expert system to predict effects of noise pollution on operators of power plant using neuro-fuzzy approach.

    Science.gov (United States)

    Ahmed, Hameed Kaleel; Zulquernain, Mallick

    2009-01-01

    Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible design parameters in minimum possible time. This study aims to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, exposure time, and age of the operators doing complex type of task.

  9. Wind speed and wind power short and medium range predictions for complex terrain using artificial neural networks and ensemble calibration

    Science.gov (United States)

    Schicker, Irene; Papazek, Petrina; Kann, Alexander; Wang, Yong

    2017-04-01

    Reliable predictions of wind speed and wind power are vital for balancing the electricity network. Within the last two decades the amount of energy stemming from renewable sources increased substantially relying heavily on the prevailing synoptic conditions. Especially for regions with complex terrain and forested surfaces providing reliable predictions is a challenging task. Forecasts in the nowcasting as well as in the (two) day-ahead range are thus essential for the network balancing. Predictions of wind speed and wind power from the nowcasting to the +72-hour forecast range using NWP models in regions with complex terrain need a suitable horizontal, vertical and temporal resolution (e.g. 10 - 15 minute forecasts for the Nowcasting range) requiring high performance computing. To be able to provide sub-hourly to hourly forecasts different approaches such as model output statistics (MOS) or artificial neural networks (ANN) - including feed forward recurrent neural networks, fuzzy logic, particle swarm optimizations - are needed as computational costs are too high. To represent the forecast uncertainties additional probabilistic ensemble predictions are required increasing the computational needs. Ensemble prediction systems account for errors and uncertainties in the initial and boundary conditions, parameterizations, numeric, etc. Due to the underestimation of model and sampling errors ensemble predictions tend to be underdispersive and biased. They lack, too, sharpness and reliability. These shortcomings can be accounted for using statistical post-processing methods such as the non-homogeneous Gaussian regression (NGR) to calibrate an ensemble. These calibrated ensembles provide forecasts in the medium range for any arbitrary location where observations are available. In this study an ANN is used to provide forecasts for the nowcasting and medium-range with sub-hourly to hourly predictions for different Austrian sites, including high alpine sites as well as low

  10. Study on the methodology for predicting and preventing errors to improve reliability of maintenance task in nuclear power plant

    International Nuclear Information System (INIS)

    Hanafusa, Hidemitsu; Iwaki, Toshio; Embrey, D.

    2000-01-01

    The objective of this study was to develop and effective methodology for predicting and preventing errors in nuclear power plant maintenance tasks. A method was established by which chief maintenance personnel can predict and reduce errors when reviewing the maintenance procedures and while referring to maintenance supporting systems and methods in other industries including aviation and chemical plant industries. The method involves the following seven steps: 1. Identification of maintenance tasks. 2. Specification of important tasks affecting safety. 3. Assessment of human errors occurring during important tasks. 4. Identification of Performance Degrading Factors. 5. Dividing important tasks into sub-tasks. 6. Extraction of errors using Predictive Human Error Analysis (PHEA). 7. Development of strategies for reducing errors and for recovering from errors. By way of a trial, this method was applied to the pump maintenance procedure in nuclear power plants. This method is believed to be capable of identifying the expected errors in important tasks and supporting the development of error reduction measures. By applying this method, the number of accidents resulting form human errors during maintenance can be reduced. Moreover, the maintenance support base using computers was developed. (author)

  11. Using micro saint to predict performance in a nuclear power plant control room

    International Nuclear Information System (INIS)

    Lawless, M.T.; Laughery, K.R.; Persenky, J.J.

    1995-09-01

    The United States Nuclear Regulatory Commission (NRC) requires a technical basis for regulatory actions. In the area of human factors, one possible technical basis is human performance modeling technology including task network modeling. This study assessed the feasibility and validity of task network modeling to predict the performance of control room crews. Task network models were built that matched the experimental conditions of a study on computerized procedures that was conducted at North Carolina State University. The data from the open-quotes paper proceduresclose quotes conditions were used to calibrate the task network models. Then, the models were manipulated to reflect expected changes when computerized procedures were used. These models' predictions were then compared to the experimental data from the open-quotes computerized conditionsclose quotes of the North Carolina State University study. Analyses indicated that the models predicted some subsets of the data well, but not all. Implications for the use of task network modeling are discussed

  12. Tracing and Prediction of Losses for Deregulated Operation of Power Systems

    DEFF Research Database (Denmark)

    Nallagownden, Perumal; Mukerjee, Ravindra Nath; Masri, Syafrudin

    2011-01-01

    To facilitate both generation and retailing to have an open access to the transmission grid for trading electricity, a real time procedure is proposed. The line flows for an operation are assumed to be available from PMU and WAM validated state estimation. Power tracing determines different...

  13. The Predictive Power of Adult Attachment Patterns on Interpersonal Cognitive Distortions of University Students

    Science.gov (United States)

    Sirin, Hatice Deveci

    2017-01-01

    The purpose of this study is to determine the explanatory power of the anxious and avoidant dimensions of attachment to explain the interpersonal cognitive distortions. The research was conducted on correlational pattern, one of the quantitative research models. A total of 413 volunteer undergraduates students, from Selçuk University were research…

  14. Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series

    Czech Academy of Sciences Publication Activity Database

    Keil, P.; Herben, Tomáš; Rosindell, J.; Storch, D.

    2010-01-01

    Roč. 265, č. 1 (2010), s. 68-86 ISSN 0022-5193 R&D Projects: GA MŠk LC06073 Institutional research plan: CEZ:AV0Z60050516 Keywords : Taylor´s power law * density dependence * pink noise Subject RIV: EF - Botanics Impact factor: 2.371, year: 2010

  15. Rational Design of Lanthanoid Single-Ion Magnets: Predictive Power of the Theoretical Models.

    Science.gov (United States)

    Baldoví, José J; Duan, Yan; Morales, Roser; Gaita-Ariño, Alejandro; Ruiz, Eliseo; Coronado, Eugenio

    2016-09-12

    We report two new single-ion magnets (SIMs) of a family of oxydiacetate lanthanide complexes with D3 symmetry to test the predictive capabilities of complete active space ab initio methods (CASSCF and CASPT2) and the semiempirical radial effective charge (REC) model. Comparison of the theoretical predictions of the energy levels, wave functions and magnetic properties with detailed spectroscopic and magnetic characterisation is used to critically discuss the limitations of these theoretical approaches. The need for spectroscopic information for a reliable description of the properties of lanthanide SIMs is emphasised. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. Stream power framework for predicting geomorphic change: The 2013 Colorado Front Range flood

    Science.gov (United States)

    Yochum, Steven E.; Sholtes, Joel S.; Scott, Julian A.; Bledsoe, Brian P.

    2017-09-01

    The Colorado Front Range flood of September 2013 induced a diverse range of geomorphic changes along numerous stream corridors, providing an opportunity to assess responses to a large flood in a semiarid landscape. We defined six classes of geomorphic change related to peak unit stream power and valley confinement for 531 stream reaches over 226 km, spanning a gradient of channel scales and slope. Geomorphic change was generally driven by erosion of channel margins in confined reaches and by a combination of deposition and erosion in unconfined reaches. The magnitude of geomorphic change typically increased with unit stream power (ω), with greater responses observed in unconfined channels. Cumulative logit modeling indicated that total stream power or unit stream power, unit stream power gradient, and valley confinement are significant predictors of geomorphic response for this flood event. Based on this dataset, thresholds for geomorphic adjustment were defined. For channel slopes 230 W/m2 (16 lb/ft-s; at least 10% of the investigated sites experienced substantial channel widening) and a credible potential for avulsions, braiding, and loss of adjacent road embankments associated with ω > 480 W/m2 (33 lb/ft-s; at least 10% of the investigated sites experienced such geomorphic change). Infrequent to numerous eroded banks were very likely with ω > 700 W/m2 (48 lb/ft-s), with substantial channel widening or major geomorphic change shifting from credible to likely. Importantly, in reaches where there were large reductions in ω as the valley form shifted from confined to relatively unconfined, large amounts of deposition-induced, reach-scale geomorphic change occurred in some locations at relatively low ω. Additionally, alluvial channels with slopes > 3% had greater resistance to geomorphic change, likely caused by armoring by larger bed material and increased flow resistance from enhanced bedforms. Finally, we describe how these results can potentially be used by

  17. The Predictive Power of Phonemic Awareness and Naming Speed for Early Dutch Word Recognition

    Science.gov (United States)

    Verhagen, Wim G. M.; Aarnoutse, Cor A. J.; van Leeuwe, Jan F. J.

    2009-01-01

    Effects of phonemic awareness and naming speed on the speed and accuracy of Dutch children's word recognition were investigated in a longitudinal study. Both the speed and accuracy of word recognition at the end of Grade 2 were predicted by naming speed from both kindergarten and Grade 1, after control for autoregressive relations, kindergarten…

  18. Modelling for Understanding AND for Prediction/Classification--The Power of Neural Networks in Research

    Science.gov (United States)

    Cascallar, Eduardo; Musso, Mariel; Kyndt, Eva; Dochy, Filip

    2014-01-01

    Two articles, Edelsbrunner and, Schneider (2013), and Nokelainen and Silander (2014) comment on Musso, Kyndt, Cascallar, and Dochy (2013). Several relevant issues are raised and some important clarifications are made in response to both commentaries. Predictive systems based on artificial neural networks continue to be the focus of current…

  19. Predictive Power for Program Success from Engineering and Manufacturing Development Performance Trends

    National Research Council Canada - National Science Library

    Gailey, Charles

    2002-01-01

    ...; they were descriptive rather than predictive. It was also found that the Selective Acquisition Reporting system had succeeded in identifying the "bad" programs; but corrective measures, if any, were ineffective. Additional research indicated that the contract type most likely to lead to success in EMD was Cost Plus Incentive Fee.

  20. Turbine Control Strategy using Wave Prediction to Optimise Power Take Off of Overtopping Wave Energy Converters

    DEFF Research Database (Denmark)

    Tedd, James; Knapp, Wilfried; Frigaard, Peter

    2005-01-01

    . The concept of including an element of prediction, based on wave records a short distance in front of the Wave Dragon, is introduced. Initial simulations indicate a possibility to increase production by 5 to 10 % with knowledge of the next five overtopping events. It is intended to further this research...

  1. Predictability of the Power Output of Three Wave Energy Technologies in the Danish North Sea

    DEFF Research Database (Denmark)

    Chozas, Julia Fernandez; Jensen, N. E. Helstrup; Sørensen, H. C.

    2011-01-01

    The paper addresses an important challenge ahead the integration of the electricity generated by wave energy conversion technologies into the electric grid. Particularly, it looks into the role of wave energy within the day-ahead electricity market. For that the predictability of the theoretical...

  2. Predicting speech intelligibility in adverse conditions: evaluation of the speech-based envelope power spectrum model

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2011-01-01

    conditions by comparing predictions to measured data from [Kjems et al. (2009). J. Acoust. Soc. Am. 126 (3), 1415-1426] where speech is mixed with four different interferers, including speech-shaped noise, bottle noise, car noise, and cafe noise. The model accounts well for the differences in intelligibility...

  3. Predicting First-Year Student Success in Learning Communities: The Power of Pre-College Variables

    Science.gov (United States)

    Sperry, Rita A.

    2015-01-01

    The study used pre-college variables in the prediction of retention and probation status of first-year students in learning communities at a regional public university in South Texas. The correlational study employed multivariate analyses on data collected from the campus registrar about three consecutive cohorts (N = 4,215) of first-year…

  4. The Predictive Power of Undergraduates' Personality Traits and Self-Esteem Regarding Their Forgiveness

    Science.gov (United States)

    Yalcin, S. Barbaros; Kavakli, Mehmet; Kesici, Sahin

    2017-01-01

    Purpose: The purpose of this study is to determine whether or not the undergraduates' personality traits and self-esteem predict their forgiveness. Methods: The study was conducted using a descriptive research designed as a relational survey method. The study group consists of 323 undergraduates, of whom 250 (77.2 %) are female and 73 (22.5%) are…

  5. Thermal fatigue life prediction based on crack propagation behaviors in high-temperature materials for power plant components

    International Nuclear Information System (INIS)

    Nitta, Akihito; Ogata, Takashi; Kuwabara, Kazuo

    1986-01-01

    For reducing an electric power supply cost, it is desired to extend the life of thermal power plant being still supplying the greater part of electric power in Japan. It is, therefore, becoming more and more important for the remaining life control of long-operated thermal power plants to exactly estimate the thermal fatigue damage accumulating in high temperature components. In this report, a discussion was made on thermal fatigue life laws derived from the crack propagation laws. As a result, the life laws were found to be effective for the evaluation of thermal fatigue life as well as isothermal fatigue life. Based on the concept of the life laws, the thermal and isothermal fatigue lives were also predicted as a propagation period of a crack with initial length equal to grain size from the characteristics of high temperature fatigue crack propagation. In addition to them, the rapid straining method was found to be required for more accurate estimation of creep strain in in-phase thermal fatigue. (author)

  6. The use of different ensemble forecasting systems for wind power prediction on a real case in the South of Italy

    DEFF Research Database (Denmark)

    Alessandrini, Stefano; Sperati, Simone; Pinson, Pierre

    2012-01-01

    Short-term forecasting applied to wind energy is becoming increasingly important due to the constant growth of this renewable source, whose uncertainty requires a constant effort to meet the needs of the national electrical systems and their operators. Regarding to this, the probabilistic approach...... the data to wind energy: the spread calculated on wind power can then be used as an accuracy predictor due to its level of correlation with the deterministic WPF error. In this presentation we investigate the performances for both wind power and accuracy prediction of the new EPS used at the ECMWF, whose...... horizontal resolution was increased on January 2010 from 60 km to 32 km, on a complex terrain area already used in previous studies and located in Southern Italy. The work consists in the use of the ECMWF deterministic model in a WPF approach followed by a recursive feed-forward Neural Networks (NN...

  7. Assessment of Gasoline Prices and its Predictive Power on U.S. Consumers' Retail Spending and Savings

    Science.gov (United States)

    Alvarado-Bonilla, Joel

    The rising costs of fuels and specifically gasoline pose an economic challenge to U.S. consumers. Thus, the specific problem considered in this study was a rise in gasoline prices can reduce consumer spending, disposable income, food service traffic, and spending on healthy food, medicines, or visits to the doctor. Aligned with the problem, the purpose of this quantitative multiple correlation study was to examine the economic aspects for a rise in gasoline prices to reduce the six elements in the problem. This study consisted of a correlational design based on a retrospective longitudinal analysis (RLA) to examine gasoline prices versus the economic indexes of: (a) Retail Spending and (b) personal savings (PS). The RLA consisted on historic archival public data from 1978 to 2015. This RLA involved two separate linear multiple regression analyses to measure gasoline price's predictive power (PP) on two indexes while controlling for Unemployment Rate (UR). In summary, regression Formula 1 revealed Gasoline Price had a significant 61.1% PP on Retail Spending. In contrast, Formula 2 had Gasoline Price not having a significant PP on PS. Formula 2 yielded UR with 38.8% PP on PS. Results were significant at pSpending means a spending link to retail items such as: food service traffic, healthy food, medicines, and consumer spending. The UR predictive power on PS was unexpected, but logical from an economic view. Also unexpected was Gasoline Price's non-predictive power on PS, which suggests Americans may not save money when gasoline prices drop. These results shed light on the link of gasoline and UR on U.S. consumer's economy through savings and spending, which can be useful for policy design on gasoline and fuels taxing and pricing. The results serve as a basis for future study on gasoline and economics.

  8. Heterogeneity in predictive power of early childhood nutritional indicators for mid-childhood outcomes: Evidence from Vietnam.

    Science.gov (United States)

    Duc, Le Thuc; Behrman, Jere R

    2017-08-01

    We utilize longitudinal data on nearly 1800 children in Vietnam to study the predictive power of alternative measures of early childhood undernutrition for outcomes at age eight years: weight-for-age (WAZ8), height-for-age (HAZ8), and education (reading, math and receptive vocabulary). We apply two-stage procedures to derive unpredicted weight gain and height growth in the first year of life. Our estimates show that a standard deviation (SD) increase in birth weight is associated with an increase of 0.14 (standard error [SE]: 0.03) in WAZ8 and 0.12 (SE: 0.02) in HAZ8. These are significantly lower than the corresponding figures for a SD increase in unpredicted weight gain: 0.51 (SE: 0.02) and 0.33 (SE: 0.02). The heterogeneity of the predictive power of early childhood nutrition indicators for mid-childhood outcomes reflects both life-cycle considerations (prenatal versus postnatal) and the choice of anthropometric measure (height versus weight). Even though all the nutritional indicators that involve postnatal nutritional status are important predictors for all the mid-childhood outcomes, there are some important differences between the indicators on weight and height. The magnitude of associations with the outcomes is one aspect of the heterogeneity. More importantly there is a component of height-for-age z-score (at age 12 months) that adds predictive power for all the mid-childhood outcomes beyond that of birth weight and weight gain in the first year of life. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  9. Agility performance in high-level junior basketball players: the predictive value of anthropometrics and power qualities.

    Science.gov (United States)

    Sisic, Nedim; Jelicic, Mario; Pehar, Miran; Spasic, Miodrag; Sekulic, Damir

    2016-01-01

    In basketball, anthropometric status is an important factor when identifying and selecting talents, while agility is one of the most vital motor performances. The aim of this investigation was to evaluate the influence of anthropometric variables and power capacities on different preplanned agility performances. The participants were 92 high-level, junior-age basketball players (16-17 years of age; 187.6±8.72 cm in body height, 78.40±12.26 kg in body mass), randomly divided into a validation and cross-validation subsample. The predictors set consisted of 16 anthropometric variables, three tests of power-capacities (Sargent-jump, broad-jump and medicine-ball-throw) as predictors. The criteria were three tests of agility: a T-Shape-Test; a Zig-Zag-Test, and a test of running with a 180-degree turn (T180). Forward stepwise multiple regressions were calculated for validation subsamples and then cross-validated. Cross validation included correlations between observed and predicted scores, dependent samples t-test between predicted and observed scores; and Bland Altman graphics. Analysis of the variance identified centres being advanced in most of the anthropometric indices, and medicine-ball-throw (all at Pagility performance, but leg length is found to be negatively associated with performance in basketball-specific agility. Power capacities are confirmed to be an important factor in agility. The results highlighted the importance of sport-specific tests when studying pre-planned agility performance in basketball. The improvement in power capacities will probably result in an improvement in agility in basketball athletes, while anthropometric indices should be used in order to identify those athletes who can achieve superior agility performance.

  10. EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task

    Science.gov (United States)

    de Groot, Annette M. B.; Hofman, Winni F.; Hillen, Marij A.

    2016-01-01

    As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is—partly due to a lack of theory-driven research—no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck’s theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence). Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and exact

  11. EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task.

    Directory of Open Access Journals (Sweden)

    Mats B Küssner

    Full Text Available As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is-partly due to a lack of theory-driven research-no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck's theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence. Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and

  12. EEG Beta Power but Not Background Music Predicts the Recall Scores in a Foreign-Vocabulary Learning Task.

    Science.gov (United States)

    Küssner, Mats B; de Groot, Annette M B; Hofman, Winni F; Hillen, Marij A

    2016-01-01

    As tantalizing as the idea that background music beneficially affects foreign vocabulary learning may seem, there is-partly due to a lack of theory-driven research-no consistent evidence to support this notion. We investigated inter-individual differences in the effects of background music on foreign vocabulary learning. Based on Eysenck's theory of personality we predicted that individuals with a high level of cortical arousal should perform worse when learning with background music compared to silence, whereas individuals with a low level of cortical arousal should be unaffected by background music or benefit from it. Participants were tested in a paired-associate learning paradigm consisting of three immediate word recall tasks, as well as a delayed recall task one week later. Baseline cortical arousal assessed with spontaneous EEG measurement in silence prior to the learning rounds was used for the analyses. Results revealed no interaction between cortical arousal and the learning condition (background music vs. silence). Instead, we found an unexpected main effect of cortical arousal in the beta band on recall, indicating that individuals with high beta power learned more vocabulary than those with low beta power. To substantiate this finding we conducted an exact replication of the experiment. Whereas the main effect of cortical arousal was only present in a subsample of participants, a beneficial main effect of background music appeared. A combined analysis of both experiments suggests that beta power predicts the performance in the word recall task, but that there is no effect of background music on foreign vocabulary learning. In light of these findings, we discuss whether searching for effects of background music on foreign vocabulary learning, independent of factors such as inter-individual differences and task complexity, might be a red herring. Importantly, our findings emphasize the need for sufficiently powered research designs and exact replications

  13. Prediction of speech masking release for fluctuating interferers based on the envelope power signal-to-noise ratio

    DEFF Research Database (Denmark)

    Jørgensen, Søren; Dau, Torsten

    2012-01-01

    to conditions with stationary interferers due to the long-term estimation of the envelope power and cannot account for the well-known phenomenon of speech masking release. Here, a short-term version of the sEPSM is described [Jørgensen and Dau, 2012, in preparation], which estimates the SNRenv in short temporal...... segments. Predictions obtained with the short-term sEPSM are compared to data from Kjems et al. [(2009). J. Acoust. Soc. Am. 126 (3), 1415-1426] where speech is mixed with four different interferers, including speech-shaped noise, bottle noise, car noise, and a highly non-stationary cafe noise. The model...

  14. Prediction of a Newbuilding Proce of the Bulk Carriers based on Gross Tonnage GT and Main Engine Power

    Science.gov (United States)

    Cepowska, Żaneta; Cepowski, Tomasz

    2017-03-01

    The paper presents mathematical relationships that allow us to forecast the newbuilding price of new bulk carriers, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the price based on a gross tonnage capacity and a main engine power The approximations were developed using linear regression and the theory of artificial neural networks. The presented relations have practical application for estimation of bulk carrier newbuilding price needed in preliminary parametric design of the ship. It follows from the above that the use of artificial neural networks to predict the price of a bulk carrier brings more accurate solutions than linear regression.

  15. On the databases and automated system for prediction of the power-genrating plant strength and operating life

    International Nuclear Information System (INIS)

    Tyurin, O.S.; Petushkov, V.A.; Romanov, A.N.

    1986-01-01

    Problems on strength and operating life computer calculation of structural elements of automated power generating plants which operate under conditions of cyclic loading are considered. Structural charts and organization of the problem-oriented databases and information systems are discussed and analyzed. A variant of the database organization is suggested for the automated system of strength calculations. Methods for calculation of stress-strained states and temperature fields of the structural elements are described as dependent on actual operating conditions, plotting of the loading history and prediction of the life at the stages up to the crack initiation and development

  16. The effect of viscosity on the maximisation of electrical power from a wave energy converter under predictive control

    OpenAIRE

    O'Sullivan, Adrian C. M.; Lightbody, Gordon

    2017-01-01

    In this paper, the non-linear effects of viscosity on the performance of a Wave Energy Converter (WEC) system are analysed. A standard linear Model Predictive Control (MPC) is used to show the negative effects that the unaccounted non-linear viscosity force in the hydrodynamic system has on the power absorption. A non-linear MPC (NLMPC) is then implemented, where the non-linear viscosity effects are included in the optimisation. A linear drag coefficient estimate of the non-linear viscosity i...

  17. Quantification of power consumption and oxygen transfer characteristics of a stirred miniature bioreactor for predictive fermentation scale-up.

    Science.gov (United States)

    Gill, N K; Appleton, M; Baganz, F; Lye, G J

    2008-08-15

    Miniature parallel bioreactors are becoming increasingly important as tools to facilitate rapid bioprocess design. Once the most promising strain and culture conditions have been identified a suitable scale-up basis needs to be established in order that the cell growth rates and product yields achieved in small scale optimization studies are maintained at larger scales. Recently we have reported on the design of a miniature stirred bioreactor system capable of parallel operation [Gill et al. (2008); Biochem Eng J 39:164-176]. In order to enable the predictive scale-up of miniature bioreactor results the current study describes a more detailed investigation of the bioreactor mixing and oxygen mass transfer characteristics and the creation of predictive engineering correlations useful for scale-up studies. A Power number of 3.5 for the miniature turbine impeller was first established based on experimental ungassed power consumption measurements. The variation of the measured gassed to ungassed power ratio, P(g)/P(ug), was then shown to be adequately predicted by existing correlations proposed by Cui et al. [Cui et al. (1996); Chem Eng Sci 51:2631-2636] and Mockel et al. [Mockel et al. (1990); Acta Biotechnol 10:215-224]. A correlation relating the measured oxygen mass transfer coefficient, k(L)a, to the gassed power per unit volume and superficial gas velocity was also established for the miniature bioreactor. Based on these correlations a series of scale-up studies at matched k(L)a (0.06-0.11 s(-1)) and P(g)/V (657-2,960 W m(-3)) were performed for the batch growth of Escherichia coli TOP10 pQR239 using glycerol as a carbon source. Constant k(L)a was shown to be the most reliable basis for predictive scale-up of miniature bioreactor results to conventional laboratory scale. This gave good agreement in both cell growth and oxygen utilization kinetics over the range of k(L)a values investigated. The work described here thus gives further insight into the performance

  18. Genomic prediction and genomic variance partitioning of daily and residual feed intake in pigs using Bayesian Power Lasso models

    DEFF Research Database (Denmark)

    Do, Duy Ngoc; Janss, L. L. G.; Strathe, Anders Bjerring

    of different power parameters had no effect on predictive ability. Partitioning of genomic variance showed that SNP groups either by position (intron, exon, downstream, upstream and 5’UTR) or by function (missense and protein-altering) had similar average explained variance per SNP, except that 3’UTR had...... genomic variance for RFI and daily feed intake (DFI). A total of 1272 Duroc pigs had both genotypic and phenotypic records for these traits. Significant SNPs were detected on chromosome 1 (SSC 1) and SSC 14 for RFI and on SSC 1 for DFI. BPL models had similar accuracy and bias as GBLUP method but use...

  19. Multivariable predictive control considering time delay for load-frequency control in multi-area power systems

    Directory of Open Access Journals (Sweden)

    Daniar Sabah

    2016-12-01

    Full Text Available In this paper, a multivariable model based predictive control (MPC is proposed for the solution of load frequency control (LFC in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.

  20. The nuclear power plant maintenance personnel reliability prediction (NPP/MPRP) effort at Oak Ridge National Laboratory

    International Nuclear Information System (INIS)

    Knee, H.E.; Haas, P.M.; Siegel, A.I.

    1982-01-01

    Human errors committed during maintenance activities are potentially a major contribution to the overall risk associated with the operation of a nuclear power plant (NPP). An NRC-sponsored program at Oak Ridge National Laboratory is attempting to develop a quantitative predictive technique to evaluate the contribution of maintenance errors to the overall NPP risk. The current work includes a survey of the requirements of potential users to ascertain the need for and content of the proposed quantitative model, plus an initial job/task analysis to determine the scope and applicability of various maintenance tasks. In addition, existing human reliability prediction models are being reviewed and assessed with respect to their applicability to NPP maintenance tasks. This paper discusses the status of the program and summarizes the results to date

  1. Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Chan, Yea-Kuang; Tsai, Yu-Ching [Institute of Nuclear Energy Research, Taoyuan City, Taiwan (China). Nuclear Engineering Division

    2017-03-15

    The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.

  2. Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant

    International Nuclear Information System (INIS)

    Chan, Yea-Kuang; Tsai, Yu-Ching

    2017-01-01

    The objective of this study is to develop a turbine cycle model using the multiple regression approach to estimate the turbine-generator output for the Chinshan Nuclear Power Plant (NPP). The plant operating data was verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the multiple regression based turbine cycle model. The proposed model was used to estimate the turbine-generator output. The effectiveness of the proposed turbine cycle model was demonstrated by using plant operating data obtained from the Chinshan NPP Unit 2. The results show that this multiple regression based turbine cycle model can be used to accurately estimate the turbine-generator output. In addition, this study also provides an alternative approach with simple and easy features to evaluate the thermal performance for nuclear power plants.

  3. Thermo-mechanical behavior of power electronic packaging assemblies: From characterization to predictive simulation of lifetimes

    Science.gov (United States)

    Dalverny, O.; Alexis, J.

    2018-02-01

    This article deals with thermo-mechanical behavior of power electronic modules used in several transportation applications as railway, aeronautic or automotive systems. Due to a multi-layered structures, involving different materials with a large variation of coefficient of thermal expansion, temperature variations originated from active or passive cycling (respectively from die dissipation or environmental constraint) induces strain and stresses field variations, giving fatigue phenomenon of the system. The analysis of the behavior of these systems and their dimensioning require the implementation of complex modeling strategies by both the multi-physical and the multi-scale character of the power modules. In this paper we present some solutions for studying the thermomechanical behavior of brazed assemblies as well as taking into account the interfaces represented by the numerous metallizations involved in the process assembly.

  4. Performance Prediction and Simulation of Gas Turbine Engine Operation for Aircraft, Marine, Vehicular, and Power Generation

    Science.gov (United States)

    2007-02-01

    Single Shaft Gas Turbine . Constant TIT, B-159 Operation with Different Fuels and Water Injection Figure B.147 Schematic Representation of a Twin Spool ...Function of the Amount of Injected B-169 Steam for a Single Shaft Gas Turbine Figure B.155 Change of Compressor Pressure Ratio with Water Injection...Water Injection, for a Twin Shaft Gas Turbine B-171 Figure B.158 Range of Variation of Power Deviation for Existing Gas Turbines

  5. Performance predictions and measurements for space-power-system heat pipes

    International Nuclear Information System (INIS)

    Prenger, F.C. Jr.

    1981-01-01

    High temperature liquid metal heat pipes designed for space power systems have been analyzed and tested. Three wick designs are discussed and a design rationale for the heat pipe is provided. Test results on a molybdenum, annular wick heat pipe are presented. Performance limitations due to boiling and capillary limits are presented. There is evidence that the vapor flow in the adiabatic section is turbulent and that the transition Reynolds number is 4000

  6. Evaluation of Millstone Nuclear Power Plant, Environmental Impact prediction, based on monitoring programs

    Energy Technology Data Exchange (ETDEWEB)

    Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

    1977-02-01

    This report evaluates the nonradiological monitoring program at Millstone Nuclear Power Plant. Both operational as well as preoperational monitoring programs were analyzed to produce long-term (5 yr or longer) data sets, where possible. In order to determine the effectiveness of these monitoring programs, the appropriate data sets have to be analyzed by the appropriate statistical analysis. Thus, both open literature and current statistical analysis being developed at Pacific Northwest Laboratories (PNL) were employed in data analysis.

  7. Predicting speech intelligibility based on a correlation metric in the envelope power spectrum domain

    DEFF Research Database (Denmark)

    Relaño-Iborra, Helia; May, Tobias; Zaar, Johannes

    2016-01-01

    by the short-time objective intelligibility measure [STOI; Taal, Hendriks, Heusdens, and Jensen (2011). IEEE Trans. Audio Speech Lang. Process. 19(7), 2125–2136]. This “hybrid” model, named sEPSMcorr, is shown to account for the effects of stationary and fluctuating additive interferers as well...... as for the effects of non-linear distortions, such as spectral subtraction, phase jitter, and ideal time frequency segregation (ITFS). The model shows a broader predictive range than both the original mr-sEPSM (which fails in the phase-jitter and ITFS conditions) and STOI (which fails to predict the influence......, the model might be valuable for evaluating the effects of a large range of interferers and distortions on speech intelligibility, including consequences of hearing impairment and hearing-instrument signal processing....

  8. Maintenance personnel performance simulation (MAPPS): a model for predicting maintenance performance reliability in nuclear power plants

    International Nuclear Information System (INIS)

    Knee, H.E.; Krois, P.A.; Haas, P.M.; Siegel, A.I.; Ryan, T.G.

    1983-01-01

    The NRC has developed a structured, quantitative, predictive methodology in the form of a computerized simulation model for assessing maintainer task performance. Objective of the overall program is to develop, validate, and disseminate a practical, useful, and acceptable methodology for the quantitative assessment of NPP maintenance personnel reliability. The program was organized into four phases: (1) scoping study, (2) model development, (3) model evaluation, and (4) model dissemination. The program is currently nearing completion of Phase 2 - Model Development

  9. Evaluation of Monticello Nuclear Power Plant, Environmental Impact Prediction, based on monitoring programs

    Energy Technology Data Exchange (ETDEWEB)

    Gore, K.L.; Thomas, J.M.; Kannberg, L.D.; Watson, D.G.

    1976-11-01

    This report evaluates quantitatively the nonradiological environmental monitoring programs at Monticello Nuclear Generating Plant. The general objective of the study is to assess the effectiveness of monitoring programs in the measurement of environmental impacts. Specific objectives include the following: (1) Assess the validity of environmental impact predictions made in the Environmental Statement by analysis of nonradiological monitoring data; (2) evaluate the general adequacy of environmental monitoring programs for detecting impacts and their responsiveness to Technical Specifications objectives; (3) assess the adequacy of preoperational monitoring programs in providing a sufficient data base for evaluating operational impacts; (4) identify possible impacts that were not predicted in the environmental statement and identify monitoring activities that need to be added, modified or deleted; and (5) assist in identifying environmental impacts, monitoring methods, and measurement problems that need additional research before quantitative predictions can be attempted. Preoperational as well as operational monitoring data were examined to test the usefulness of baseline information in evaluating impacts. This included an examination of the analytical methods used to measure ecological and physical parameters, and an assessment of sampling periodicity and sensitivity where appropriate data were available.

  10. Cognitive distortions in an acutely traumatized sample: an investigation of predictive power and neural correlates.

    Science.gov (United States)

    Daniels, J K; Hegadoren, K; Coupland, N J; Rowe, B H; Neufeld, R W J; Lanius, R A

    2011-10-01

    Current theories of post-traumatic stress disorder (PTSD) place considerable emphasis on the role cognitive distortions such as self-blame, hopelessness or preoccupation with danger play in the etiology and maintenance of the disorder. Previous studies have shown that cognitive distortions in the early aftermath of traumatic events can predict future PTSD severity but, to date, no studies have investigated the neural correlates of this association. We conducted a prospective study with 106 acutely traumatized subjects, assessing symptom severity at three time points within the first 3 months post-trauma. A subsample of 20 subjects additionally underwent a functional 4-T magnetic resonance imaging (MRI) scan at 2 to 4 months post-trauma. Cognitive distortions proved to be a significant predictor of concurrent symptom severity in addition to diagnostic status, but did not predict future symptom severity or diagnostic status over and above the initial symptom severity. Cognitive distortions were correlated with blood oxygen level-dependent (BOLD) signal strength in brain regions previously implicated in visual processing, imagery and autobiographic memory recall. Intrusion characteristics accounted for most of these correlations. This investigation revealed significant predictive value of cognitive distortions concerning concurrent PTSD severity and also established a significant relationship between cognitive distortions and neural activations during trauma recall in an acutely traumatized sample. These data indicate a direct link between the extent of cognitive distortions and the intrusive nature of trauma memories.

  11. Wind assessment and power prediction from a wind farm in southern Saskatchewan

    Science.gov (United States)

    Chakravarthy, Mukundhan

    Mesoscale and Microscale Modeling are two methods used to estimate wind energy resources. The main parameters of wind resource estimation are the mean wind speed and the mean wind power density. Mesoscale Modeling was applied to three different regions, Regina, Saskatoon, and Gull Lake, located in southern Saskatchewan, Canada. The areas were selected as centers of a domain for a grid with a horizontal resolution of 3 kilometers. Mesoscale Modeling was performed using the software tool, Anemoscope. Wind resources for the regions and the areas surrounding them have been generated for three elevations (30, 50, and 80 meters). As it is a site for a large wind turbine farm, the region in and around Swift Current in southern Saskatchewan (approximately 36 km x 36 km in area) was the site of choice for this study in Microscale Modeling. A widely popular software, WAsP, was chosen to perform the study. Statistical wind data was obtained from a Swift Current meteorological station over a period of ten years (2000-2009). A wind resource grid has been set up for the area at a horizontal resolution of 200 meters, and wind resource maps have been generated for heights of 50, 65, and 80 meters above ground level as the heights are the potential wind turbine hub heights. In order to simulate the SaskPower Centennial Wind Power Station, a wind farm was set up with 83 wind turbines in the Coulee Municipality region near Swift Current. The annual energy production for the entire farm, along with those of the individual turbines, has been calculated. Both total and individual wind turbine productions were accurately modeled.

  12. The Power of Micro-Blogging: How to Use Twitter for Predicting the Stock Market

    Directory of Open Access Journals (Sweden)

    Francesco Corea

    2015-11-01

    Full Text Available The availability of new data and techniques enriched the existing extensive literature on the importance of investors’ sentiment and on his impact of the stock price oscillations. The purpose of this paper is to exploit micro-blogging data in order to construct a new index-tracking variable that may be used to earn some insights on the Nasdaq-100’s future movements. The results are promising: the models augmented with the newly created variable show an incremented explanatory power with respect to the benchmark.

  13. Model Predictive Control of Offshore Power Stations With Waste Heat Recovery

    DEFF Research Database (Denmark)

    Pierobon, Leonardo; Chan, Richard; Li, Xiangan

    2016-01-01

    Rankine cycle (ORC) turbogenerator to increase the energy conversion efficiency. Results show a potential reduction of frequency drop up to 40%for a step in the load set-point of 4 MW, compared to proportional–integral control systems. Fuel savings in the range of 2–3% are also expected by optimizing on...... control (MPC) coupled with a steady-state performance optimizer has been developed in the SIMULINK language and is documented in the paper. The test case is the regulation of a power system serving an oil and gas platform in the Norwegian Sea. One of the three gas turbines is combined with an organic...

  14. The Power of Exercise-Induced T-wave Alternans to Predict Ventricular Arrhythmias in Patients with Implanted Cardiac Defibrillator

    Directory of Open Access Journals (Sweden)

    Laura Burattini

    2013-01-01

    Full Text Available The power of exercise-induced T-wave alternans (TWA to predict the occurrence of ventricular arrhythmias was evaluated in 67 patients with an implanted cardiac defibrillator (ICD. During the 4-year follow-up, electrocardiographic (ECG tracings were recorded in a bicycle ergometer test with increasing workload ranging from zero (NoWL to the patient's maximal capacity (MaxWL. After the follow-up, patients were classified as either ICD_Cases (n = 29, if developed ventricular tachycardia/fibrillation, or ICD_Controls (n = 38. TWA was quantified using our heart-rate adaptive match filter. Compared to NoWL, MaxWL was characterized by faster heart rates and higher TWA in both ICD_Cases (12-18 μ V vs. 20-39 μ V; P < 0.05 and ICD_Controls (9-15 μ V vs. 20-32 μ V; P < 0.05. Still, TWA was able to discriminate the two ICD groups during NoWL (sensitivity = 59-83%, specificity = 53-84% but not MaxWL (sensitivity = 55-69%, specificity = 39-74%. Thus, this retrospective observational case-control study suggests that TWA's predictive power for the occurrence of ventricular arrhythmias could increase at low heart rates.

  15. Transfer of infrared thermography predictive maintenance technologies to Soviet-designed nuclear power plants: experience at Chernobyl

    Science.gov (United States)

    Pugh, Ray; Huff, Roy

    1999-03-01

    The importance of infrared (IR) technology and analysis in today's world of predictive maintenance and reliability- centered maintenance cannot be understated. The use of infrared is especially important in facilities that are required to maintain a high degree of equipment reliability because of plant or public safety concerns. As with all maintenance tools, particularly those used in predictive maintenance approaches, training plays a key role in their effectiveness and the benefit gained from their use. This paper details an effort to transfer IR technology to Soviet- designed nuclear power plants in Russia, Ukraine, and Lithuania. Delivery of this technology and post-delivery training activities have been completed recently at the Chornobyl nuclear power plant in Ukraine. Many interesting challenges were encountered during this effort. Hardware procurement and delivery of IR technology to a sensitive country were complicated by United States regulations. Freight and shipping infrastructure and host-country customs policies complicated hardware transport. Training activities were complicated by special hardware, software and training material translation needs, limited communication opportunities, and site logistical concerns. These challenges and others encountered while supplying the Chornobyl plant with state-of-the-art IR technology are described in this paper.

  16. Prediction of Maintenance Period of Equipment Through Risk Assessment of Thermal Power Plants

    International Nuclear Information System (INIS)

    Song, Gee Wook; Kim, Bum Shin; Choi, Woo Song; Park, Myung Soo

    2013-01-01

    Risk-based inspection (RBI) is a well-known method that is used to optimize inspection activities based on risk analysis in order to identify the high-risk components of major facilities such as power plants. RBI, when implemented and maintained properly, improves plant reliability and safety while reducing unplanned outages and repair costs. Risk is given by the product of the probability of failure (Pof) and the consequence of failure (COF). A semi-quantitative method is generally used for risk assessment. Semi-quantitative risk assessment complements the low accuracy of qualitative risk assessment and the high expense and long calculation time of quantitative risk assessment. The first step of RB I is to identify important failure modes and causes in the equipment. Once these are defined, the Pof and COF can be assessed for each failure. During Pof and COF assessment, an effective inspection method and range can be easily found. In this paper, the calculation of the Pof is improved for accurate risk assessment. A modified semi-quantitative risk assessment was carried out for boiler facilities of thermal power plants, and the next maintenance schedules for the equipment were decided

  17. High Fidelity, “Faster than Real-Time” Simulator for Predicting Power System Dynamic Behavior - Final Technical Report

    Energy Technology Data Exchange (ETDEWEB)

    Flueck, Alex [Illinois Inst. of Technology, Chicago, IL (United States)

    2017-07-14

    The “High Fidelity, Faster than Real­Time Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of large­scale power system dynamics simulation, including (1) a validated faster than real­ time simulation of both stable and unstable transient dynamics in a large­scale positive sequence transmission grid model, (2) a three­phase unbalanced simulation platform for modeling new grid devices, such as independently controlled single­phase static var compensators (SVCs), (3) the world’s first high fidelity three­phase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a first­of­its­ kind implementation of a single­phase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the long­term, the simulator will form the backbone of the newly conceived hybrid real­time protection and control architecture that will coordinate local controls, wide­area measurements, wide­area controls and advanced real­time prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the faster­than­real­time simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three­ phase unbalanced simulator’s ability to

  18. Transient stability enhancement of modern power grid using predictive Wide-Area Monitoring and Control

    Science.gov (United States)

    Yousefian, Reza

    This dissertation presents a real-time Wide-Area Control (WAC) designed based on artificial intelligence for large scale modern power systems transient stability enhancement. The WAC using the measurements available from Phasor Measurement Units (PMUs) at generator buses, monitors the global oscillations in the system and optimally augments the local excitation system of the synchronous generators. The complexity of the power system stability problem along with uncertainties and nonlinearities makes the conventional modeling non-practical or inaccurate. In this work Reinforcement Learning (RL) algorithm on the benchmark of Neural Networks (NNs) is used to map the nonlinearities of the system in real-time. This method different from both the centralized and the decentralized control schemes, employs a number of semi-autonomous agents to collaborate with each other to perform optimal control theory well-suited for WAC applications. Also, to handle the delays in Wide-Area Monitoring (WAM) and adapt the RL toward the robust control design, Temporal Difference (TD) is proposed as a solver for RL problem or optimal cost function. However, the main drawback of such WAC design is that it is challenging to determine if an offline trained network is valid to assess the stability of the power system once the system is evolved to a different operating state or network topology. In order to address the generality issue of NNs, a value priority scheme is proposed in this work to design a hybrid linear and nonlinear controllers. The algorithm so-called supervised RL is based on mixture of experts, where it is initialized by linear controller and as the performance and identification of the RL controller improves in real-time switches to the other controller. This work also focuses on transient stability and develops Lyapunov energy functions for synchronous generators to monitor the stability stress of the system. Using such energies as a cost function guarantees the convergence

  19. The honeymoon effect in job performance - Temporal increases in the predictive power of achievement motivation

    Science.gov (United States)

    Helmreich, Robert L.; Sawin, Linda L.; Carsrud, Alan L.

    1986-01-01

    Correlations between a job performance criterion and personality measures reflecting achievement motivation and an interpersonal orientation were examined at three points in time after completion of job training for a sample of airline reservations agents. Although correlations between the personality predictors and performance were small and nonsignificant for the 3-month period after beginning the job, by the end of six and eight months a number of significant relationships had emerged. Implications for the utility of personality measures in selection and performance prediction are discussed.

  20. CD8 and CD4 epitope predictions in RV144: no strong evidence of a T-cell driven sieve effect in HIV-1 breakthrough sequences from trial participants.

    Science.gov (United States)

    Dommaraju, Kalpana; Kijak, Gustavo; Carlson, Jonathan M; Larsen, Brendan B; Tovanabutra, Sodsai; Geraghty, Dan E; Deng, Wenjie; Maust, Brandon S; Edlefsen, Paul T; Sanders-Buell, Eric; Ratto-Kim, Silvia; deSouza, Mark S; Rerks-Ngarm, Supachai; Nitayaphan, Sorachai; Pitisuttihum, Punnee; Kaewkungwal, Jaranit; O'Connell, Robert J; Robb, Merlin L; Michael, Nelson L; Mullins, James I; Kim, Jerome H; Rolland, Morgane

    2014-01-01

    The modest protection afforded by the RV144 vaccine offers an opportunity to evaluate its mechanisms of protection. Differences between HIV-1 breakthrough viruses from vaccine and placebo recipients can be attributed to the RV144 vaccine as this was a randomized and double-blinded trial. CD8 and CD4 T cell epitope repertoires were predicted in HIV-1 proteomes from 110 RV144 participants. Predicted Gag epitope repertoires were smaller in vaccine than in placebo recipients (p = 0.019). After comparing participant-derived epitopes to corresponding epitopes in the RV144 vaccine, the proportion of epitopes that could be matched differed depending on the protein conservation (only 36% of epitopes in Env vs 84-91% in Gag/Pol/Nef for CD8 predicted epitopes) or on vaccine insert subtype (55% against CRF01_AE vs 7% against subtype B). To compare predicted epitopes to the vaccine, we analyzed predicted binding affinity and evolutionary distance measurements. Comparisons between the vaccine and placebo arm did not reveal robust evidence for a T cell driven sieve effect, although some differences were noted in Env-V2 (0.022≤p-value≤0.231). The paucity of CD8 T cell responses identified following RV144 vaccination, with no evidence for V2 specificity, considered together both with the association of decreased infection risk in RV 144 participants with V-specific antibody responses and a V2 sieve effect, lead us to hypothesize that this sieve effect was not T cell specific. Overall, our results did not reveal a strong differential impact of vaccine-induced T cell responses among breakthrough infections in RV144 participants.

  1. Feasibility of High-Power Diode Laser Array Surrogate to Support Development of Predictive Laser Lethality Model

    Energy Technology Data Exchange (ETDEWEB)

    Lowdermilk, W H; Rubenchik, A M; Springer, H K

    2011-01-13

    Predictive modeling and simulation of high power laser-target interactions is sufficiently undeveloped that full-scale, field testing is required to assess lethality of military directed-energy (DE) systems. The cost and complexity of such testing programs severely limit the ability to vary and optimize parameters of the interaction. Thus development of advanced simulation tools, validated by experiments under well-controlled and diagnosed laboratory conditions that are able to provide detailed physics insight into the laser-target interaction and reduce requirements for full-scale testing will accelerate development of DE weapon systems. The ultimate goal is a comprehensive end-to-end simulation capability, from targeting and firing the laser system through laser-target interaction and dispersal of target debris; a 'Stockpile Science' - like capability for DE weapon systems. To support development of advanced modeling and simulation tools requires laboratory experiments to generate laser-target interaction data. Until now, to make relevant measurements required construction and operation of very high power and complex lasers, which are themselves costly and often unique devices, operating in dedicated facilities that don't permit experiments on targets containing energetic materials. High power diode laser arrays, pioneered by LLNL, provide a way to circumvent this limitation, as such arrays capable of delivering irradiances characteristic of De weapon requires are self-contained, compact, light weight and thus easily transportable to facilities, such as the High Explosives Applications Facility (HEAF) at Lawrence Livermore National Laboratory (LLNL) where testing with energetic materials can be performed. The purpose of this study was to establish the feasibility of using such arrays to support future development of advanced laser lethality and vulnerability simulation codes through providing data for materials characterization and laser

  2. Prediction Model of Photovoltaic Module Temperature for Power Performance of Floating PVs

    Directory of Open Access Journals (Sweden)

    Waithiru Charles Lawrence Kamuyu

    2018-02-01

    Full Text Available Rapid reduction in the price of photovoltaic (solar PV cells and modules has resulted in a rapid increase in solar system deployments to an annual expected capacity of 200 GW by 2020. Achieving high PV cell and module efficiency is necessary for many solar manufacturers to break even. In addition, new innovative installation methods are emerging to complement the drive to lower $/W PV system price. The floating PV (FPV solar market space has emerged as a method for utilizing the cool ambient environment of the FPV system near the water surface based on successful FPV module (FPVM reliability studies that showed degradation rates below 0.5% p.a. with new encapsulation material. PV module temperature analysis is another critical area, governing the efficiency performance of solar cells and module. In this paper, data collected over five-minute intervals from a PV system over a year is analyzed. We use MATLAB to derived equation coefficients of predictable environmental variables to derive FPVM’s first module temperature operation models. When comparing the theoretical prediction to real field PV module operation temperature, the corresponding model errors range between 2% and 4% depending on number of equation coefficients incorporated. This study is useful in validation results of other studies that show FPV systems producing 10% more energy than other land based systems.

  3. Predictive power of the severity measure of attachment loss for periodontal care need.

    Science.gov (United States)

    Liu, Honghu; Marcus, Marvin; Maida, Carl A; Wang, Yan; Shen, Jie; Spolsky, Vladimir W

    2013-10-01

    The prevalence of periodontal diseases is high, and >15% of adults have severe gum disease. Clinical attachment loss (AL) is one of the most important measures for periodontal disease severity. With AL, one could measure the worst scenario, the average, or the cumulative sum of AL among all teeth. The objective of this study is to evaluate which of the 15 measures of periodontal problems (e.g., maximum, mean, and cumulative AL) best predict the need for periodontal treatment. Using detailed periodontal data obtained through clinical examination from the National Health and Nutrition Examination Survey 1999 to 2002, weighted logistic regression was used to model the periodontal treatment need of 15 different periodontal disease measures. The outcome measure is the clinically determined periodontal need. After adjustment for the covariates of age, sex, ethnicity, education, smoking status, and diabetes, the three most predictive measures were identified as: 1) the sum of the maximum mid-buccal (B) and mesio-buccal (MB) measures, which reflects the worst case of both B and MB measures; 2) the sum of the maximum MB measure or the worst case of the MB measure; and 3) the sum of all B and MB measures, or the cumulative AL measures. Cumulative periodontal morbidity, particularly the worst case of B and MB measures, has the strongest impact on the need for periodontal care. All the demographic variables and covariates follow the classic pattern of association with periodontal disease.

  4. An investigation of engine performance parameters and artificial intelligent emission prediction of hydrogen powered car

    International Nuclear Information System (INIS)

    Ho, Tien; Karri, Vishy; Lim, Daniel; Barret, Danny

    2008-01-01

    With the depletion of fossil fuel resources and the potential consequences of climate change due to fossil fuel use, much effort has been put into the search for alternative fuels for transportation. Although there are several potential alternative fuels, which have low impact on the environment, none of these fuels have the ability to be used as the sole 'fuel of the future'. One fuel which is likely to become a part of the over all solution to the transportation fuel dilemma is hydrogen. In this paper, The Toyota Corolla four cylinder, 1.8 l engine running on petrol is systematically converted to run on hydrogen. Several ancillary instruments for measuring various engine operating parameters and emissions are fitted to appraise the performance of the hydrogen car. The effect of hydrogen as a fuel compares with gasoline on engine operating parameters and effect of engine operating parameters on emission characteristics is discussed. Based on the experimental setup, a suite of neural network models were tested to accurately predict the effect of major engine operating conditions on the hydrogen car emissions. Predictions were found to be ±4% to the experimental values. This work provided better understanding of the effect of engine process parameters on emissions. (author)

  5. EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response.

    Science.gov (United States)

    Olbrich, Sebastian; Arns, Martijn

    2013-10-01

    Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.

  6. The predictive power of dividend yields for future inflation: Money illusion or rational causes?

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    slope coefficients that increase numerically with the horizon in regressions of future inflation onto the dividend yield, in accordance with the data. A purely rational version of the model with no money illusion, but with a link from expected inflation to real consumption growth, also generates......In long-term US data the stock market dividend yield is a strong predictor of long-horizon inflation with a negative slope coefficient. This finding is puzzling in light of the traditional Modigliani-Cohn money illusion hypothesis according to which the dividend yield varies positively...... with expected inflation. To rationalize the finding we develop a consumption-based model with recursive preferences and money illusion. The model with reasonable values of risk aversion and intertemporal elasticity of substitution, and either rational or adaptive expectations, implies significantly negative...

  7. Power and loads for wind turbines in yawed conditions. Analysis of field measurements and aerodynamic predictions

    Energy Technology Data Exchange (ETDEWEB)

    Boorsma, K. [ECN Wind Energy, Petten (Netherlands)

    2012-11-15

    A description is given of the work carried out within the framework of the FLOW (Far and Large Offshore Wind) project on single turbine performance in yawed flow conditions. Hereto both field measurements as well as calculations with an aerodynamic code are analyzed. The rotors of horizontal axis wind turbines follow the changes in the wind direction for optimal performance. The reason is that the power is expected to decrease for badly oriented rotors. So, insight in the effects of the yaw angle on performance is important for optimization of the yaw control of each individual turbine. The effect of misalignment on performance and loads of a single 2.5 MW wind turbine during normal operation is investigated. Hereto measurements at the ECN Wind Turbine Test Site Wieringermeer (EWTW) are analyzed from December 2004 until April 2009. Also, the influence of yaw is studied using a design code and results from this design code are compared with wind tunnel measurements.

  8. Predictive power of task orientation, general self-efficacy and self-determined motivation on fun and boredom

    Directory of Open Access Journals (Sweden)

    Lorena Ruiz-González

    2015-12-01

    Full Text Available Abstract The aim of this study was to test the predictive power of dispositional orientations, general self-efficacy and self-determined motivation on fun and boredom in physical education classes, with a sample of 459 adolescents between 13 and 18 with a mean age of 15 years (SD = 0.88. The adolescents responded to four Likert scales: Perceptions of Success Questionnaire, General Self-Efficacy Scale, Sport Motivation Scale and Intrinsic Satisfaction Questionnaire in Sport. The results showed the structural regression model showed that task orientation and general self-efficacy positively predicted self-determined motivation and this in turn positively predicted more fun and less boredom in physical education classes. Consequently, the promotion of an educational task-oriented environment where learners perceive their progress and make them feel more competent, will allow them to overcome the intrinsically motivated tasks, and therefore they will have more fun. Pedagogical implications for less boredom and more fun in physical education classes are discussed.

  9. Factor structure, evolution, and predictive power of emotional competencies on physical and emotional health in the elderly.

    Science.gov (United States)

    Fantini-Hauwel, Carole; Mikolajczak, Moïra

    2014-09-01

    Emotional competence (EC) has been found to be an important predictor of individuals' health. While it is well known that EC predicts important outcomes in young adults, its importance is less clear in the elderly. We aimed to address this gap: Is the structure of EC the same in older as in younger adults? How do EC evolve between 50 and 80 years old? Does the predictive power of EC, regarding physical and emotional adjustment, increase or decrease with age? A total of 6,688 participants filled subjective health and EC questionnaires. We gathered their medication consumption over the last 11 years, from the database of health insurance. While the structure of ECs remains stable in older adults, it generally declines as people get older, except for emotion regulation, which improves with age. Results also show that EC predicts both physical and emotional health. These results suggest that the development of specific interventions to improve EC may be useful for the elderly. © The Author(s) 2014.

  10. Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues

    Science.gov (United States)

    Möhler, Christian; Russ, Tom; Wohlfahrt, Patrick; Elter, Alina; Runz, Armin; Richter, Christian; Greilich, Steffen

    2018-01-01

    An experimental setup for consecutive measurement of ion and x-ray absorption in tissue or other materials is introduced. With this setup using a 3D-printed sample container, the reference stopping-power ratio (SPR) of materials can be measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine tissue samples were prepared for measurement, comprising five samples each of 13 tissue types representing about 80% of the total body mass (three different muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone). Using a standard stoichiometric calibration for single-energy CT (SECT) as well as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all tissues and then compared to the measured reference. With the SECT approach, the SPRs of all tissues were predicted with a mean error of (-0.84  ±  0.12)% and a mean absolute error of (1.27  ±  0.12)%. In contrast, the DECT-based SPR predictions were overall consistent with the measured reference with a mean error of (-0.02  ±  0.15)% and a mean absolute error of (0.10  ±  0.15)%. Thus, in this study, the potential of DECT to decrease range uncertainty could be confirmed in biological tissue.

  11. Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Sigeti, David E. [Los Alamos National Laboratory; Pelak, Robert A. [Los Alamos National Laboratory

    2012-09-11

    We present a Bayesian statistical methodology for identifying improvement in predictive simulations, including an analysis of the number of (presumably expensive) simulations that will need to be made in order to establish with a given level of confidence that an improvement has been observed. Our analysis assumes the ability to predict (or postdict) the same experiments with legacy and new simulation codes and uses a simple binomial model for the probability, {theta}, that, in an experiment chosen at random, the new code will provide a better prediction than the old. This model makes it possible to do statistical analysis with an absolute minimum of assumptions about the statistics of the quantities involved, at the price of discarding some potentially important information in the data. In particular, the analysis depends only on whether or not the new code predicts better than the old in any given experiment, and not on the magnitude of the improvement. We show how the posterior distribution for {theta} may be used, in a kind of Bayesian hypothesis testing, both to decide if an improvement has been observed and to quantify our confidence in that decision. We quantify the predictive probability that should be assigned, prior to taking any data, to the possibility of achieving a given level of confidence, as a function of sample size. We show how this predictive probability depends on the true value of {theta} and, in particular, how there will always be a region around {theta} = 1/2 where it is highly improbable that we will be able to identify an improvement in predictive capability, although the width of this region will shrink to zero as the sample size goes to infinity. We show how the posterior standard deviation may be used, as a kind of 'plan B metric' in the case that the analysis shows that {theta} is close to 1/2 and argue that such a plan B should generally be part of hypothesis testing. All the analysis presented in the paper is done with a

  12. Model predictive control system and method for integrated gasification combined cycle power generation

    Science.gov (United States)

    Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

    2013-04-09

    Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

  13. A theoretical prediction of critical heat flux in subcooled pool boiling during power transients

    International Nuclear Information System (INIS)

    Pasamehmetoglu, K.O.; Nelson, R.A.; Gunnerson, F.S.

    1988-01-01

    Understanding and predicting critical heat flux (CHF) behavior during steady-state and transient conditions are of fundamenatal interest in the design, operation, safety of boiling and two-phase flow devices. This paper discusses the results of a comprehensive theoretical study made specifically to model transient CHF behavior in subcooled pool boiling. This study is based upon a simplified steady-state CHF model in terms of the vapor mass growth period. The results obtained from this theory indicate favorable agreement with the experimental data from cylindrical heaters with small radii. The statistical nature of the vapor mass behavior in transient boiling also is considered and upper and lower limits for the current theory are established. Various factors that affect the discrepancy between the data and the theory are discussed

  14. The future is in the numbers: the power of predictive analysis in the biomedical educational environment

    Science.gov (United States)

    Gullo, Charles A.

    2016-01-01

    Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to do poorly on standardized national exams and who may need remediation. In this article, we provide a step-by-step approach that schools can utilize to generate data that are useful when predicting the future performance of current students in any given program. We discuss the use of linear regression analysis as the means of generating that data and highlight some of the limitations. Finally, we lament on how the combination of these institution-specific data sets are not being fully utilized at the national level where these data could greatly assist programs at large. PMID:27374246

  15. The future is in the numbers: the power of predictive analysis in the biomedical educational environment

    Directory of Open Access Journals (Sweden)

    Charles A. Gullo

    2016-07-01

    Full Text Available Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to do poorly on standardized national exams and who may need remediation. In this article, we provide a step-by-step approach that schools can utilize to generate data that are useful when predicting the future performance of current students in any given program. We discuss the use of linear regression analysis as the means of generating that data and highlight some of the limitations. Finally, we lament on how the combination of these institution-specific data sets are not being fully utilized at the national level where these data could greatly assist programs at large.

  16. Thermogravimetric analysis coupled with chemometrics as a powerful predictive tool for ß-thalassemia screening.

    Science.gov (United States)

    Risoluti, Roberta; Materazzi, Stefano; Sorrentino, Francesco; Maffei, Laura; Caprari, Patrizia

    2016-10-01

    β-Thalassemia is a hemoglobin genetic disorder characterized by the absence or reduced β-globin chain synthesis, one of the constituents of the adult hemoglobin tetramer. In this study the possibility of using thermogravimetric analysis (TGA) followed by chemometrics as a new approach for β-thalassemia detection is proposed. Blood samples from patients with β-thalassemia were analyzed by the TG7 thermobalance and the resulting curves were compared to those typical of healthy individuals. Principal Component Analysis (PCA) was used to evaluate the correlation between the hematological parameters and the thermogravimetric results. The thermogravimetric profiles of blood samples from β-thalassemia patients were clearly distinct from those of healthy individuals as result of the different quantities of water content and corpuscular fraction. The hematological overview showed significant decreases in the values of red blood cell indices and an increase in red cell distribution width value in thalassemia subjects when compared with those of healthy subjects. The implementation of a predictive model based on Partial Least Square Discriminant Analysis (PLS-DA) for β-thalassemia diagnosis, was performed and validated. This model permitted the discrimination of anemic patients and healthy individuals and was able to detect thalassemia in clinically heterogeneous patients as in the presence of δβ-thalassemia and β-thalassemia combined with Hb Lepore. TGA and Chemometrics are capable of predicting ß-thalassemia syndromes using only a few microliters of blood without any pretreatment and with an hour of analysis time. A fast, rapid and cost-effective diagnostic tool for the β-thalassemia screening is proposed. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Liquidity crisis detection: An application of log-periodic power law structures to default prediction

    Science.gov (United States)

    Wosnitza, Jan Henrik; Denz, Cornelia

    2013-09-01

    We employ the log-periodic power law (LPPL) to analyze the late-2000 financial crisis from the perspective of critical phenomena. The main purpose of this study is to examine whether LPPL structures in the development of credit default swap (CDS) spreads can be used for default classification. Based on the different triggers of Bear Stearns’ near bankruptcy during the late-2000 financial crisis and Ford’s insolvency in 2009, this study provides a quantitative description of the mechanism behind bank runs. We apply the Johansen-Ledoit-Sornette (JLS) positive feedback model to explain the rise of financial institutions’ CDS spreads during the global financial crisis 2007-2009. This investigation is based on CDS spreads of 40 major banks over the period from June 2007 to April 2009 which includes a significant CDS spread increase. The qualitative data analysis indicates that the CDS spread variations have followed LPPL patterns during the global financial crisis. Furthermore, the univariate classification performances of seven LPPL parameters as default indicators are measured by Mann-Whitney U tests. The present study supports the hypothesis that discrete scale-invariance governs the dynamics of financial markets and suggests the application of new and fast updateable default indicators to capture the buildup of long-range correlations between creditors.

  18. Factors Influencing the Predictive Power of Models for Predicting Mortality and/or Heart Failure Hospitalization in Patients With Heart Failure

    NARCIS (Netherlands)

    Ouwerkerk, Wouter; Voors, Adriaan A.; Zwinderman, Aeilko H.

    2014-01-01

    The present paper systematically reviews and compares existing prediction models in order to establish the strongest variables, models, and model characteristics in patients with heart failure predicting outcome. To improve decision making accurately predicting mortality and heart-failure

  19. Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks.

    Science.gov (United States)

    Zou, Tengyue; Lin, Shouying; Feng, Qijie; Chen, Yanlian

    2016-01-04

    Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks' activities in an uninterrupted and efficient manner.

  20. Energy Optimal Tracking Control with Discrete Fluid Power Systems using Model Predictive Control

    DEFF Research Database (Denmark)

    Hansen, Anders Hedegaard; Asmussen, Magnus Færing; Bech, Michael Møller

    2017-01-01

    For Discrete Displacement Cylinder (DDC) drives the control task lies in choosing force level. Hence, which force level to apply and thereby which pressure level each cylinder chambers shall be connected to. The DDC system is inherently a force system why often a force reference is generated by a...... and compared to a PID like tracking controller combined with a FSA. The results indicate that the energy efficiency of position tracking DDC systems may be improved significantly by using the MPC algorithm.......For Discrete Displacement Cylinder (DDC) drives the control task lies in choosing force level. Hence, which force level to apply and thereby which pressure level each cylinder chambers shall be connected to. The DDC system is inherently a force system why often a force reference is generated...... by a tracking controller and translated into a discrete force level in a Force Shifting Algorithm (FSA). In the current paper the tracking controller and the FSA are combined in a Model Predictive Control algorithm solving the tracking problem while minimizing the energy use. Two MPC algorithms are investigated...

  1. Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tengyue Zou

    2016-01-01

    Full Text Available Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.

  2. Energy-Efficient Control with Harvesting Predictions for Solar-Powered Wireless Sensor Networks

    Science.gov (United States)

    Zou, Tengyue; Lin, Shouying; Feng, Qijie; Chen, Yanlian

    2016-01-01

    Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner. PMID:26742042

  3. Combined individual scrummaging kinetics and muscular power predict competitive team scrum success.

    Science.gov (United States)

    Green, Andrew; Dafkin, Chloe; Kerr, Samantha; McKinon, Warrick

    2017-09-01

    Scrummaging is a major component of Rugby Union gameplay. Successful scrummaging is dependent on the coordination of the forward players and the strength of the eight individuals. The study aim was to determine whether individual scrummaging kinetics and other candidate factors associated with scrummaging performance discriminate team scrum performances. Sixteen club-level forwards (stature: 1.80 ± 0.1 m; mass: 99.0 ± 18.2 kg) were initially divided into two scrummaging packs. A total of 10 various scrum permutations were tested, where players were randomly swapped between the two packs. Winning scrums were determined by two observers on opposite sides of the scrum. Fatigue (100 mm visual analogue scale (VAS)) and scrummaging effort (6-20 rating of perceived exertion (RPE)) were assessed following each scrum contest. Individual scrummaging kinetics were acquired through an instrumented scrum ergometer and muscular power indicated through vertical jump heights. Student's t-tests were used to differentiate between winning and losing scrum packs. VAS and RPE were assessed using repeated measures ANOVAs. Winning scrum packs had significantly larger combined force magnitudes (p scrum packs. While perceived VAS and RPE values progressively increased (p scrum magnitudes were observed between the 1st and 10th scrum (p = .418). The results indicated that the combination of individual forces, variation in movement and factors related to scrummaging performance, such as vertical jump height, were associated with team scrummaging success.

  4. A SEASONAL AND MONTHLY APPROACH FOR PREDICTING THE DELIVERED ENERGY QUANTITY IN A PHOTOVOLTAIC POWER PLANT IN ROMANIA

    Directory of Open Access Journals (Sweden)

    George Căruțașu

    2016-12-01

    Full Text Available In this paper, we present solutions that facilitate the forecasting of the delivered energy quantity in a photovoltaic power plant using the data measured from the solar panels' sensors: solar irradiation level, present module temperature, environmental temperature, atmospheric pressure and humidity. We have developed and analyzed a series of Artificial Neural Networks (ANNs based on the Levenberg-Marquardt algorithm, using seasonal and monthly approaches. We have also integrated our developed Artificial Neural Networks into callable functions that we have compiled using the Matlab Compiler SDK. Thus, our solution can be accessed by developers through multiple Application Programming Interfaces when programming software that predicts the photovoltaic renewable energy production considering the seasonal particularities of the Romanian weather patterns

  5. Experimental and Theoretical Investigations to Improve the Predictive Power of Nuclear Reaction Models in Spallation Neutron Production

    International Nuclear Information System (INIS)

    Nuenighoff, K.; Filges, D.; Goldenbaum, F.; Neef, R.D.; Nuenighoff, K.; Paul, N.; Schaal, H.; Sterzenbach, G.; Wohlmuther, M.; Enke, M.; Herbach, C.M.; Hilscher, D.; Jahnke, U.; Tishchenko, V.; Galin, J.; Letourneau, A.; Lott, B.; Peghaire, A.; Pienkowski, L.; Schroeder, U.; Toke, J.; Tietze, A.

    2002-01-01

    In order to design the target station of the European Spallation Source ESS measurements of the neutron production in possible target materials like Pb, Hg and W were performed. The aim of these measurements was to validate the simulation codes and to proof their predictive power. The NESSI experiment allows for the comparison of neutron multiplicity distributions between experiment and simulation, which is a much more sensitive method than only comparing the mean neutron multiplicities. An agreement of better than 10 % was achieved. Various target geometries with diameters up to 15 cm and lengths up to 35 cm over a range from 0.4 till 2.5 GeV incident proton energy were studied. The Monte-Carlo Simulations were performed with the HERMES, LCS, and MCNPX code system. (authors)

  6. Predicting the Impact of Climate Change on U.S. Power Grids and Its Wider Implications on National Security

    Energy Technology Data Exchange (ETDEWEB)

    Wong, Pak C.; Leung, Lai-Yung R.; Lu, Ning; Paget, Maria L.; Correia, James; Jiang, Wei; Mackey, Patrick S.; Taylor, Zachary T.; Xie, YuLong; Xu, Jianhua; Unwin, Stephen D.; Sanfilippo, Antonio P.

    2009-03-23

    We discuss our technosocial analytics research and devel-opment on predicting and assessing the impact of climate change on U.S. power-grids and the wider implications for national security. The ongoing efforts extend cutting-edge modeling theories derived from climate, energy, social sciences, and national security domains to form a unified system coupled with an interactive visual interface for technosocial analysis. The goal of the system is to create viable future scenarios that address both technical and social factors involved in the model domains. These scenarios enable policy makers to formulate a coherent, unified strategy towards building a safe and secure society. The paper gives an executive summary of our efforts in the past fiscal year and provides a glimpse of our work planned for the second year of the three-year project being conducted at the Pacific Northwest National Laboratory.

  7. Market powers predict reciprocal grooming in golden snub-nosed monkeys (Rhinopithecus roxellana.

    Directory of Open Access Journals (Sweden)

    Wei Wei

    Full Text Available Social grooming is a common form of affiliative behavior in primates. Biological market theory suggests that grooming can be traded either for grooming or other social commodities and services. When no other services are exchanged, grooming is predicted to be approximately reciprocated within a dyad. In contrast, the amount of reciprocal grooming should decrease as other offered services increase. We studied grooming patterns between polygamous male and female in golden snub-nosed monkeys (Rhinopithecus roxellana from the Qinling Mountains of central China and found that about 29.7% of grooming bouts were reciprocated. However, the durations of grooming bouts offered and returned was asymmetrical within dyads. In bisexual dyads, more grooming was initiated by females than males, which became more pronounced as the number of females per one-male unit increased. The rate of copulation per day for each female was positively correlated with the total duration of grooming time females invested in males.. Females without an infant (non-mothers directed more grooming towards females with an infant (mothers and were significantly more likely to be non-reciprocated. There was a significant negative relationship between non-mother and mother grooming duration and the rate of infants per female in each one-male unit. High-ranking females also received more grooming from low-ranking females than vice versa. The rate of food-related aggressive interactions was per day for low-ranking females was negatively correlated with the duration of grooming that low-ranking females gave to high-ranking females. Our results showed that grooming reciprocation in R. roxellana was discrepancy. This investment-reciprocity rate could be explained by the exchange of other social services in lieu of grooming.

  8. Market powers predict reciprocal grooming in golden snub-nosed monkeys (Rhinopithecus roxellana).

    Science.gov (United States)

    Wei, Wei; Qi, Xiao-Guang; Guo, Song-Tao; Zhao, Da-Peng; Zhang, Peng; Huang, Kang; Li, Bao-Guo

    2012-01-01

    Social grooming is a common form of affiliative behavior in primates. Biological market theory suggests that grooming can be traded either for grooming or other social commodities and services. When no other services are exchanged, grooming is predicted to be approximately reciprocated within a dyad. In contrast, the amount of reciprocal grooming should decrease as other offered services increase. We studied grooming patterns between polygamous male and female in golden snub-nosed monkeys (Rhinopithecus roxellana) from the Qinling Mountains of central China and found that about 29.7% of grooming bouts were reciprocated. However, the durations of grooming bouts offered and returned was asymmetrical within dyads. In bisexual dyads, more grooming was initiated by females than males, which became more pronounced as the number of females per one-male unit increased. The rate of copulation per day for each female was positively correlated with the total duration of grooming time females invested in males.. Females without an infant (non-mothers) directed more grooming towards females with an infant (mothers) and were significantly more likely to be non-reciprocated. There was a significant negative relationship between non-mother and mother grooming duration and the rate of infants per female in each one-male unit. High-ranking females also received more grooming from low-ranking females than vice versa. The rate of food-related aggressive interactions was per day for low-ranking females was negatively correlated with the duration of grooming that low-ranking females gave to high-ranking females. Our results showed that grooming reciprocation in R. roxellana was discrepancy. This investment-reciprocity rate could be explained by the exchange of other social services in lieu of grooming.

  9. Prediction of an optimum biodiesel-diesel blended fuel for compression ignition engine using GT-power

    International Nuclear Information System (INIS)

    Shah, A.N.; Shah, F.H.; Shahid, E.M.; Gardezi, S.A.R.

    2014-01-01

    This paper describes the development of a turbocharged direct-injection compression ignition (CI) engine model using fluid-dynamic engine simulation codes through a simulating tool known as GT Power. The model was first fueled with diesel, and then with various blends of biodiesel and diesel by allotting suitable parameters to predict an optimum blended fuel. During the optimization, main focus was on the engine performance, combustion, and one of the major regulated gaseous pollutants known as oxides of nitrogen (NOx). The combustion parameters such as Premix Duration (DP), Main Duration (DM), Premix Fraction (FP), Main Exponent (EM) and ignition delay (ID) affect the start of injection (SOI) angle, and thus played significant role in the prediction of optimum blended fuel. The SOI angle ranging from 5.2 to 5.7 degree crank angle (DCA) measured before top dead center (TDC) revealed an optimum biodiesel-diesel blend known as B20 (20% biodiesel and 80% diesel by volume). B20 exhibited the minimum possible NOx emissions, better combustion and acceptable engine performance. Moreover, experiments were performed to validate the simulated results by fueling the engine with B20 fuel and operating it on AC electrical dynamometer. Both the experimental and simulated results were in good agreement revealing maximum deviations of only 3%, 3.4%, 4.2%, and 5.1% for NOx, maximum combustion pressure (MCP), engine brake power (BP), and brake specific fuel consumption (BSFC), respectively. Meanwhile, a positive correlation was found between MCP and NOx showing that both the parameters are higher at lower speeds, relative to higher engine speeds. (author)

  10. Ground-based remote sensing profiling and numerical weather prediction model to manage nuclear power plants meteorological surveillance in Switzerland

    Directory of Open Access Journals (Sweden)

    B. Calpini

    2011-08-01

    Full Text Available The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers. This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS. The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every three hours. This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plants and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release the best picture of the 24-h evolution of the air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an

  11. Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery

    Directory of Open Access Journals (Sweden)

    Yunfeng Jiang

    2016-07-01

    Full Text Available A fractional derivative system identification approach for modeling battery dynamics is presented in this paper, where fractional derivatives are applied to approximate non-linear dynamic behavior of a battery system. The least squares-based state-variable filter (LSSVF method commonly used in the identification of continuous-time models is extended to allow the estimation of fractional derivative coefficents and parameters of the battery models by monitoring a charge/discharge demand signal and a power storage/delivery signal. In particular, the model is combined by individual fractional differential models (FDMs, where the parameters can be estimated by a least-squares algorithm. Based on experimental data, it is illustrated how the fractional derivative model can be utilized to predict the dynamics of the energy storage and delivery of a lithium iron phosphate battery (LiFePO 4 in real-time. The results indicate that a FDM can accurately capture the dynamics of the energy storage and delivery of the battery over a large operating range of the battery. It is also shown that the fractional derivative model exhibits improvements on prediction performance compared to standard integer derivative model, which in beneficial for a battery management system.

  12. The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Zhiye; Liu, Mengqi [Chinese PLA General Hospital, Department of Radiology, Beijing (China); Hainan Branch of Chinese PLA General Hospital, Department of Radiology, Sanya (China); Zhou, Peng [Chinese Academy of Sciences, Research Center for Brain-inspired Intelligence, Institute of Automation, Beijing (China); University of Chinese Academy of Sciences, Beijing (China); Lv, Bin [Academy of Telecommunication Research of MIIT, Beijing (China); Wang, Yan; Wang, Yulin; Lou, Xin; Ma, Lin [Chinese PLA General Hospital, Department of Radiology, Beijing (China); Gui, Qiuping [Chinese PLA General Hospital, Department of Pathology, Beijing (China); He, Huiguang [Chinese Academy of Sciences, Research Center for Brain-inspired Intelligence, Institute of Automation, Beijing (China); University of Chinese Academy of Sciences, Beijing (China); Chinese Academy of Sciences, Center for Excellence in Brain Science and Intelligence Technology, Beijing (China)

    2017-12-15

    To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading. Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform. There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2. HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice. (orig.)

  13. The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading.

    Science.gov (United States)

    Chen, Zhiye; Zhou, Peng; Lv, Bin; Liu, Mengqi; Wang, Yan; Wang, Yulin; Lou, Xin; Gui, Qiuping; He, Huiguang; Ma, Lin

    2017-12-01

    To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading. Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform. There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2. HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice. • HFP shows positive correlation with neuroepithelial tumour grading. • HFP presents a good diagnostic efficacy for LGG and HGG. • HFP is helpful in the selection of brain tumour boundary.

  14. Strongly correlating liquids and their isomorphs

    OpenAIRE

    Pedersen, Ulf R.; Gnan, Nicoletta; Bailey, Nicholas P.; Schröder, Thomas B.; Dyre, Jeppe C.

    2010-01-01

    This paper summarizes the properties of strongly correlating liquids, i.e., liquids with strong correlations between virial and potential energy equilibrium fluctuations at constant volume. We proceed to focus on the experimental predictions for strongly correlating glass-forming liquids. These predictions include i) density scaling, ii) isochronal superposition, iii) that there is a single function from which all frequency-dependent viscoelastic response functions may be calculated, iv) that...

  15. Testing strong interaction theories

    International Nuclear Information System (INIS)

    Ellis, J.

    1979-01-01

    The author discusses possible tests of the current theories of the strong interaction, in particular, quantum chromodynamics. High energy e + e - interactions should provide an excellent means of studying the strong force. (W.D.L.)

  16. A numerical approach for size optimization and performance prediction of solar P V-hybrid power systems

    International Nuclear Information System (INIS)

    Zahedi, A.; Calia, N.

    2001-10-01

    Iran is blessed with an abundance of sunlight almost all year round. so obviously, with the right planning and strategies that are coupled to the right technology and development in the market, the potential for the new renewable energies, specially solar photovoltaic, as an alternative source of power looks promising and is constantly gaining popularity. Development and application of new renewable energy in Iran, however, is still in its infancy and will require active support by government, utilities and financing institutions. some experts might argue that Iran has plenty of natural resources like oil and gas. We should not forget, however, that even in countries with cheap fossil energy, the P V system is an economical option in supplying electricity for remote located communities and facilities. But there are good reasons suggesting that like many other countries in the world, Iran also needs to be active in utilization of sun energy. The objectives of this paper are: to give a comprehensive overview on the current solar photovoltaic energy technology. (Authors of this paper believe that Photovoltaic is the most appropriate renewable energy technology for Iran); to present the results obtained from a study which has been carried out on the size optimization, cost calculation of the photovoltaic systems for climate conditions of Iran. The method presented in this paper can be used for systems of any size and application. A further objective of this paper is to present a numerical approach for evaluating the performance of P V-Hybrid power systems. A method is developed to predict the performance of all components integrated into a P V-hybrid system. The system under investigation is a hybrid power system, in which the integrated components are P V array, a battery bank for backing up the system and a diesel generator set for supporting the battery bank. State of charge of batteries is used as a measure for the performance of the system. The running time of

  17. Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods

    International Nuclear Information System (INIS)

    Zhang, Yachao; Liu, Kaipei; Qin, Liang; An, Xueli

    2016-01-01

    Highlights: • Variational mode decomposition is adopted to process original wind power series. • A novel combined model based on machine learning methods is established. • An improved differential evolution algorithm is proposed for weight adjustment. • Probabilistic interval prediction is performed by quantile regression averaging. - Abstract: Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid

  18. Prediction and measurement of the electromagnetic environment of high-power medium-wave and short-wave broadcast antennas in far field

    International Nuclear Information System (INIS)

    Tang, Zhanghong; Wang, Qun; Ji, Zhijiang; Hou, Guoyan; Tan, Danjun; Wang, Pengqi; Shi, Meiwu; Qiu, Xianbo

    2014-01-01

    With the increasing city size, high-power electromagnetic radiation devices such as high-power medium-wave (MW) and short-wave (SW) antennas have been inevitably getting closer and closer to buildings, which resulted in the pollution of indoor electromagnetic radiation becoming worsened. To avoid such radiation exceeding the exposure limits by national standards, it is necessary to predict and survey the electromagnetic radiation by MW and SW antennas before constructing the buildings. In this paper, a modified prediction method for the far-field electromagnetic radiation is proposed and successfully applied to predict the electromagnetic environment of an area close to a group of typical high-power MW and SW wave antennas. Different from currently used simplified prediction method defined in the Radiation Protection Management Guidelines (H J/T 10. 3 -1996), the new method in this article makes use of more information such as antennas' patterns to predict the electromagnetic environment. Therefore, it improves the prediction accuracy significantly by the new feature of resolution at different directions. At the end of this article, a comparison between the prediction data and the measured results is given to demonstrate the effectiveness of the proposed new method. (authors)

  19. Prediction and measurement of the electromagnetic environment of high-power medium-wave and short-wave broadcast antennas in far field.

    Science.gov (United States)

    Tang, Zhanghong; Wang, Qun; Ji, Zhijiang; Shi, Meiwu; Hou, Guoyan; Tan, Danjun; Wang, Pengqi; Qiu, Xianbo

    2014-12-01

    With the increasing city size, high-power electromagnetic radiation devices such as high-power medium-wave (MW) and short-wave (SW) antennas have been inevitably getting closer and closer to buildings, which resulted in the pollution of indoor electromagnetic radiation becoming worsened. To avoid such radiation exceeding the exposure limits by national standards, it is necessary to predict and survey the electromagnetic radiation by MW and SW antennas before constructing the buildings. In this paper, a modified prediction method for the far-field electromagnetic radiation is proposed and successfully applied to predict the electromagnetic environment of an area close to a group of typical high-power MW and SW wave antennas. Different from currently used simplified prediction method defined in the Radiation Protection Management Guidelines (H J/T 10. 3-1996), the new method in this article makes use of more information such as antennas' patterns to predict the electromagnetic environment. Therefore, it improves the prediction accuracy significantly by the new feature of resolution at different directions. At the end of this article, a comparison between the prediction data and the measured results is given to demonstrate the effectiveness of the proposed new method. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. LDA+DMFT Approach to Magnetocrystalline Anisotropy of Strong Magnets

    Directory of Open Access Journals (Sweden)

    Jian-Xin Zhu

    2014-05-01

    Full Text Available The new challenges posed by the need of finding strong rare-earth-free magnets demand methods that can predict magnetization and magnetocrystalline anisotropy energy (MAE. We argue that correlated electron effects, which are normally underestimated in band-structure calculations, play a crucial role in the development of the orbital component of the magnetic moments. Because magnetic anisotropy arises from this orbital component, the ability to include correlation effects has profound consequences on our predictive power of the MAE of strong magnets. Here, we show that incorporating the local effects of electronic correlations with dynamical mean-field theory provides reliable estimates of the orbital moment, the mass enhancement, and the MAE of YCo_{5}.