Research on artificial neural network applications for nuclear power plants

International Nuclear Information System (INIS)

Chang, Soon-Heung; Cheon, Se-Woo

1992-01-01

Artificial neural networks (ANNs) are an emerging computational technology which can significantly enhance a number of applications. These consist of many interconnected processing elements that exhibit human-like performance, i.e., learning, pattern recognition and associative memory skills. Several application studies on ANNs devoted to nuclear power plants have been carried out at the Korea Advanced Institute of Science and Technology since 1989. These studies include the feasibility of using ANNs for the following tasks: (1) thermal power prediction, (2) transient identification, (3) multiple alarm processing and diagnosis, (4) core thermal margin prediction, and (5) prediction of core parameters for fuel reloading. This paper introduces the back-propagation network (BPN) model which is the most commonly used algorithm, and summarizes each of the studies briefly. (author)

Power Aware Simulation Framework for Wireless Sensor Networks and Nodes

Directory of Open Access Journals (Sweden)

Daniel Weber

2008-07-01

Full Text Available The constrained resources of sensor nodes limit analytical techniques and cost-time factors limit test beds to study wireless sensor networks (WSNs. Consequently, simulation becomes an essential tool to evaluate such systems.We present the power aware wireless sensors (PAWiS simulation framework that supports design and simulation of wireless sensor networks and nodes. The framework emphasizes power consumption capturing and hence the identification of inefficiencies in various hardware and software modules of the systems. These modules include all layers of the communication system, the targeted class of application itself, the power supply and energy management, the central processing unit (CPU, and the sensor-actuator interface. The modular design makes it possible to simulate heterogeneous systems. PAWiS is an OMNeT++ based discrete event simulator written in C++. It captures the node internals (modules as well as the node surroundings (network, environment and provides specific features critical to WSNs like capturing power consumption at various levels of granularity, support for mobility, and environmental dynamics as well as the simulation of timing effects. A module library with standardized interfaces and a power analysis tool have been developed to support the design and analysis of simulation models. The performance of the PAWiS simulator is comparable with other simulation environments.

An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

OpenAIRE

Wang, Weidong; Wang, Liqiang; Lu, Wei

2016-01-01

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful techniqu...

AC Power Local Network with Multiple Power Routers

Directory of Open Access Journals (Sweden)

Ryo Takahashi

2013-12-01

Full Text Available Controlling power flow and achieving appropriate matching between power sources and loads according to the quality of energy is expected to be one of the approaches to reduce wasted energy consumption. A power router, proposed recently, has the capability of realizing circuit switching in a power distribution network. This study focuses on the feasibility of an AC power routing network system composed of multiple power routers. To evaluate the feasibility, we experimentally confirm the circuit switching operation of the parallel and series configurations of the power routers, so that the network system can be designed by the combination of parallel and series configurations.

Parental Power and Adolescents' Parental Identification.

Science.gov (United States)

Acock, Alan C.; Yang, Wen Shan

1984-01-01

Combines McDonald's social power of parental identification with sex-linked models of parental identification to account for the identification of daughters (N=199) and sons (N=147) with their parents. Found that because of a halo effect, a gain in identification with one parent is not at the other parent's expense. (JAC)

Recurrent-neural-network-based identification of a cascade hydraulic actuator for closed-loop automotive power transmission control

International Nuclear Information System (INIS)

You, Seung Han; Hahn, Jin Oh

2012-01-01

By virtue of its ease of operation compared with its conventional manual counterpart, automatic transmissions are commonly used as automotive power transmission control system in today's passenger cars. In accordance with this trend, research efforts on closed-loop automatic transmission controls have been extensively carried out to improve ride quality and fuel economy. State-of-the-art power transmission control algorithms may have limitations in performance because they rely on the steady-state characteristics of the hydraulic actuator rather than fully exploit its dynamic characteristics. Since the ultimate viability of closed-loop power transmission control is dominated by precise pressure control at the level of hydraulic actuator, closed-loop control can potentially attain superior efficacy in case the hydraulic actuator can be easily incorporated into model-based observer/controller design. In this paper, we propose to use a recurrent neural network (RNN) to establish a nonlinear empirical model of a cascade hydraulic actuator in a passenger car automatic transmission, which has potential to be easily incorporated in designing observers and controllers. Experimental analysis is performed to grasp key system characteristics, based on which a nonlinear system identification procedure is carried out. Extensive experimental validation of the established model suggests that it has superb one-step-ahead prediction capability over appropriate frequency range, making it an attractive approach for model-based observer/controller design applications in automotive systems

Crack identification by artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Hwu, C.B.; Liang, Y.C. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Inst. of Aeronaut. and Astronaut.

1998-04-01

In this paper, a most popular artificial neural network called the back propagation neural network (BPN) is employed to achieve an ideal on-line identification of the crack embedded in a composite plate. Different from the usual dynamic estimate, the parameters used for the present crack identification are the strains of static deformation. It is known that the crack effects are localized which may not be clearly reflected from the boundary information especially when the data is from static deformation only. To remedy this, we use data from multiple-loading modes in which the loading modes may include the opening, shearing and tearing modes. The results show that our method for crack identification is always stable and accurate no matter how far-away of the test data from its training set. (orig.) 8 refs.

Vibration analysis in nuclear power plant using neural networks

International Nuclear Information System (INIS)

Loskiewicz-Buczak, A.; Alguindigue, I.E.

1993-01-01

Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper documents the authors' work on the design of a vibration monitoring methodology enhanced by neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to handle data which may be distorted or noisy. This paper describes three neural networks-based methods for the automation of some of the activities related to motion and vibration monitoring in engineering systems

Applying a neuro-fuzzy approach for transient identification in a nuclear power plant

International Nuclear Information System (INIS)

Costa, Rafael G.; Mol, Antonio C.A.; Pereira, Claudio M.N.A.; Carvalho, Paulo V.R.

2009-01-01

Transient identification in Nuclear Power Plant (NPP) is often a very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Several systems based on specialist systems, neural networks, and fuzzy logic have been developed for transient identification. In the work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A preliminary evaluation of the developed system was made at the Human-System Interface Laboratory (LABIHS). The obtained results show that the system can help the operators to take decisions during transients/accidents in the plant. (author)

A Heterogeneous Wireless Identification Network for the Localization of Animals Based on Stochastic Movements

Directory of Open Access Journals (Sweden)

Ivana Raos

2009-05-01

Full Text Available The improvement in the transmission range in wireless applications without the use of batteries remains a significant challenge in identification applications. In this paper, we describe a heterogeneous wireless identification network mostly powered by kinetic energy, which allows the localization of animals in open environments. The system relies on radio communications and a global positioning system. It is made up of primary and secondary nodes. Secondary nodes are kinetic-powered and take advantage of animal movements to activate the node and transmit a specific identifier, reducing the number of batteries of the system. Primary nodes are battery-powered and gather secondary-node transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. The system allows tracking based on contextual information obtained from statistical data.

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

1995-01-01

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Network-based identification of biomarkers coexpressed with multiple pathways.

Science.gov (United States)

Guo, Nancy Lan; Wan, Ying-Wooi

2014-01-01

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

Parental Identification by the Adolescent: A Social Power Approach

Science.gov (United States)

McDonald, Gerald W.

1977-01-01

A social power theory of parental identification is presented, in contrast to sex-role theories of identification, which argues that the more parental power each parent is perceived to have, the higher the degree of adolescent identification with that parent. (Author)

Sensor network based vehicle classification and license plate identification system

Energy Technology Data Exchange (ETDEWEB)

Frigo, Janette Rose [Los Alamos National Laboratory; Brennan, Sean M [Los Alamos National Laboratory; Rosten, Edward J [Los Alamos National Laboratory; Raby, Eric Y [Los Alamos National Laboratory; Kulathumani, Vinod K [WEST VIRGINIA UNIV.

2009-01-01

Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform. Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.

Neuro-diffuse algorithm for neutronic power identification of TRIGA Mark III reactor

International Nuclear Information System (INIS)

Rojas R, E.; Benitez R, J. S.; Segovia de los Rios, J. A.; Rivero G, T.

2009-10-01

In this work are presented the results of design and implementation of an algorithm based on diffuse logic systems and neural networks like method of neutronic power identification of TRIGA Mark III reactor. This algorithm uses the punctual kinetics equation as data generator of training, a cost function and a learning stage based on the descending gradient algorithm allow to optimize the parameters of membership functions of a diffuse system. Also, a series of criteria like part of the initial conditions of training algorithm are established. These criteria according to the carried out simulations show a quick convergence of neutronic power estimated from the first iterations. (Author)

Entrepreneurial Idea Identification through Online Social Networks

Science.gov (United States)

Lang, Matthew C.

2010-01-01

The increasing use of social network websites may signal a change in the way the next generation of entrepreneurs identify entrepreneurial ideas. An important part of the entrepreneurship literature emphasizes how vital the use of social networks is to entrepreneurial idea identification, opportunity recognition, and ultimately new venture…

Particle identification using artificial neural networks at BESIII

International Nuclear Information System (INIS)

Qin Gang; Lv Junguang; Bian Jianming; Chinese Academy of Sciences, Beijing

2008-01-01

A multilayered perceptrons' neural network technique has been applied in the particle identification at BESIII. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples. (authors)

Identification of neutral biochemical network models from time series data.

Science.gov (United States)

Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

2009-05-05

The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

Neural networks and their potential application to nuclear power plants

International Nuclear Information System (INIS)

Uhrig, R.E.

1991-01-01

A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters

Identification of neutral biochemical network models from time series data

Directory of Open Access Journals (Sweden)

Maia Marco

2009-05-01

Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

Directory of Open Access Journals (Sweden)

Emmanuel García

2014-01-01

Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.

Low-Power Wireless Sensor Network Infrastructures

DEFF Research Database (Denmark)

Hansen, Morten Tranberg

Advancements in wireless communication and electronics improving form factor and hardware capabilities has expanded the applicability of wireless sensor networks. Despite these advancements, devices are still limited in terms of energy which creates the need for duty-cycling and low-power protocols...... peripherals need to by duty-cycled and the low-power wireless radios are severely influenced by the environmental effects causing bursty and unreliable wireless channels. This dissertation presents a communication stack providing services for low-power communication, secure communication, data collection......, and network management which enables construction of low-power wireless sensor network applications. More specifically, these services are designed with the extreme low-power scenarios of the SensoByg project in mind and are implemented as follows. First, low-power communication is implemented with Auto...

Identification of nonlinear dynamics in power plant components using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Fernandez, B.; Tsai, W.K.

1990-01-01

Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

Development of objective flow regime identification method using self-organizing neural network

International Nuclear Information System (INIS)

Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

2004-01-01

Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

Science.gov (United States)

Gao, Zhongke; Jin, Ningde

2009-06-01

The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

An aggregated approach to harmonic modelling of loads in power distribution networks

Energy Technology Data Exchange (ETDEWEB)

Moellerstedt, E.

1998-06-01

The use of power electronics have given possibilities for more sophisticated control of power networks. This creates new demands on power network modelling. The models must not only allow for efficient and accurate simulation, but also be suitable for analysis and control design. The Harmonic Norton Equivalent presented in this thesis addresses two problems that are central in control theory, namely model reduction and system identification. It is essential to have simple representations of large systems, and there must be a way to obtain these simple models experimentally, as detailed modelling most often is too complicated. The Harmonic Norton Equivalent has its roots in the method of harmonic balance. It is a frequency domain description of loads in electric networks and describes a linear relation between the current spectrum and the voltage spectrum. The linearization implies that aggregation of loads for model reduction is a straightforward, non-iterative procedure. The models can be obtained through analytical calculations, measurements or time domain simulations. A procedure for experimental estimation of model parameters is presented. The procedure is used to estimate the parameters of a dimmer model from measurements on a real dimmer. The obtained model shows a very good agreement with validation data 24 refs, 24 figs

Social power and opinion formation in complex networks

Science.gov (United States)

Jalili, Mahdi

2013-02-01

In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts-Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts-Strogatz networks could not significantly change the consensus profile.

Neural network based electron identification in the ZEUS calorimeter

International Nuclear Information System (INIS)

Abramowicz, H.; Caldwell, A.; Sinkus, R.

1995-01-01

We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

Science.gov (United States)

Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

2018-06-01

A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

Decentralised electrical distribution network in power plants

International Nuclear Information System (INIS)

Mannila, P.; Lehtonen, M.

2000-02-01

A centralised network is a dominating network solution in today's power plants. In this study a centralised and a decentralised network were designed in order to compare them economically and technically. The emphasis of this study was on economical aspects, but also the most important technical aspects were included. The decentralised network requires less space and less cabling since there is no switchgear building and distribution transformers are placed close to the consumption in the field of a power plant. MV-motors and distribution transformers build up a ring. Less cabling and an absent switchgear building cause considerable savings. Component costs of both of the networks were estimated by using data from fulfilled power plant projects and turned out to be smaller for the decentralised network. Simulations for the decentralised network were done in order to find a way to carry out earth fault protection and location. It was found out that in high resistance earthed system the fault distance can be estimated by a relatively simple method. The decentralised network uses a field bus, which offers many new features to the automation system of a power plant. Diversified information can be collected from the protection devices in order to schedule only the needed maintenance duties at the right time. Through the field bus it is also possible to control remotely a power plant. The decentralised network is built up from ready-to-install modules. These modules are tested by the module manufacturer decreasing the need for field testing dramatically. The work contribution needed in the electrification and the management of a power plant project reduces also due the modules. During the lifetime of a power plant, maintenance is easier and more economical. (orig.)

Green Wireless Power Transfer Networks

NARCIS (Netherlands)

Liu, Q.; Golinnski, M.; Pawelczak, P.; Warnier, M.

2016-01-01

wireless power transfer network (WPTN) aims to support devices with cable-less energy on-demand. Unfortunately, wireless power transfer itself-especially through radio frequency radiation rectification-is fairly inefficient due to decaying power with distance, antenna polarization, etc.

A fusion networking model for smart grid power distribution backbone communication network based on PTN

Directory of Open Access Journals (Sweden)

Wang Hao

2016-01-01

Full Text Available In current communication network for distribution in Chinese power grid systems, the fiber communication backbone network for distribution and TD-LTE power private wireless backhaul network of power grid are both bearing by the SDH optical transmission network, which also carries the communication network of transformer substation and main electric. As the data traffic of the distribution communication and TD-LTE power private wireless network grow rapidly in recent years, it will have a big impact with the SDH network’s bearing capacity which is mainly used for main electric communication in high security level. This paper presents a fusion networking model which use a multiple-layer PTN network as the unified bearing of the TD-LTE power private wireless backhaul network and fiber communication backbone network for distribution. Network dataflow analysis shows that this model can greatly reduce the capacity pressure of the traditional SDH network as well as ensure the reliability of the transmission of the communication network for distribution and TD-LTE power private wireless network.

Optimization-based topology identification of complex networks

International Nuclear Information System (INIS)

Tang Sheng-Xue; Chen Li; He Yi-Gang

2011-01-01

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

Identification of important nodes in directed biological networks: a network motif approach.

Directory of Open Access Journals (Sweden)

Pei Wang

Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

Dynamic Frequency Control in Power Networks

OpenAIRE

Zhao, Changhong; Mallada Garcia, Enrique; Low, Steven H.

2016-01-01

Node controllers in power distribution networks in accordance with embodiments of the invention enable dynamic frequency control. One embodiment includes a node controller comprising a network interface a processor; and a memory containing a frequency control application; and a plurality of node operating parameters describing the operating parameters of a node, where the node is selected from a group consisting of at least one generator node in a power distribution network wherein the proces...

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

Science.gov (United States)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Vulnerability in the power network - a pre study

International Nuclear Information System (INIS)

Kjoelle, Gerd H.; Uhlen, Kjetil; Rolfseng, Lars; Stene, Birger

2006-02-01

Vulnerability in the power distribution network has been made a current topic because of various factors, from terror attacks to a strained power balance, large breakdowns in the power system in Europe and North America in recent time and an anticipated increase in climate-related challenges in the coming years; all related to the modern society's critical dependence on reliable power supply. Several questions are posed; whether there is a foundation to say that the vulnerability in the power network is increasing because of factors like cuts in staffing, reduced investments and increased exploitation of the capacity in the power systems, or increased average age on the air network. Different development features can indicate that the power network's ability to resist high stress is about to weaken. Examples of this is the slowly increasing trend in the number of non-reported interruptions, as well as an increase in the error frequency for power lines in the distribution network and for distribution transformers. In the pre-study there has not been found enough evidence to give a clear answer to whether the vulnerability in the power network is in fact on the rise. This is mainly due to the lack of good indicators, measuring methods and the foundation for documentation. Suggestions for methodology in order to identify unwanted incidents, estimate the probability and classify consequences for vulnerability analyses of power networks are presented. The methodology is concretised and exemplified in relation to a specific case: Power loss in the southern Norway affecting more than 250 000 people for 8-12 hours. Such a consequence is classified as critical. For four sub areas it has been exemplified which incidents may potentially cause such breaks. A summary is made of the most important challenges related to making vulnerability analyses of power networks. Comprised here are appropriate concepts, definitions, standards and measuring scales as well as data foundation

Feature-Augmented Neural Networks for Patient Note De-identification

OpenAIRE

Lee, Ji Young; Dernoncourt, Franck; Uzuner, Ozlem; Szolovits, Peter

2016-01-01

Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-t...

Plant Species Identification by Bi-channel Deep Convolutional Networks

Science.gov (United States)

He, Guiqing; Xia, Zhaoqiang; Zhang, Qiqi; Zhang, Haixi; Fan, Jianping

2018-04-01

Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

Port Hamiltonian modeling of Power Networks

NARCIS (Netherlands)

van Schaik, F.; van der Schaft, Abraham; Scherpen, Jacquelien M.A.; Zonetti, Daniele; Ortega, R

2012-01-01

In this talk a full nonlinear model for the power network in port–Hamiltonian framework is derived to study its stability properties. For this we use the modularity approach i.e., we first derive the models of individual components in power network as port-Hamiltonian systems and then we combine all

Two-component network model in voice identification technologies

Directory of Open Access Journals (Sweden)

Edita K. Kuular

2018-03-01

Full Text Available Among the most important parameters of biometric systems with voice modalities that determine their effectiveness, along with reliability and noise immunity, a speed of identification and verification of a person has been accentuated. This parameter is especially sensitive while processing large-scale voice databases in real time regime. Many research studies in this area are aimed at developing new and improving existing algorithms for presentation and processing voice records to ensure high performance of voice biometric systems. Here, it seems promising to apply a modern approach, which is based on complex network platform for solving complex massive problems with a large number of elements and taking into account their interrelationships. Thus, there are known some works which while solving problems of analysis and recognition of faces from photographs, transform images into complex networks for their subsequent processing by standard techniques. One of the first applications of complex networks to sound series (musical and speech analysis are description of frequency characteristics by constructing network models - converting the series into networks. On the network ontology platform a previously proposed technique of audio information representation aimed on its automatic analysis and speaker recognition has been developed. This implies converting information into the form of associative semantic (cognitive network structure with amplitude and frequency components both. Two speaker exemplars have been recorded and transformed into pertinent networks with consequent comparison of their topological metrics. The set of topological metrics for each of network models (amplitude and frequency one is a vector, and together  those combine a matrix, as a digital "network" voiceprint. The proposed network approach, with its sensitivity to personal conditions-physiological, psychological, emotional, might be useful not only for person identification

A neural network model for non invasive subsurface stratigraphic identification

International Nuclear Information System (INIS)

Sullivan, John M. Jr.; Ludwig, Reinhold; Lai Qiang

2000-01-01

Ground-Penetrating Radar (GRP) is a powerful tool to examine the stratigraphy below ground surface for remote sensing. Increasingly GPR has also found applications in microwave NDE as an interrogation tool to assess dielectric layers. Unfortunately, GPR data is characterized by a high degree of uncertainty and natural physical ambiguity. Robust decomposition routines are sparse for this application. We have developed a hierarchical set of neural network modules which split the task of layer profiling into consecutive stages. Successful GPR profiling of the subsurface stratigraphy is of key importance for many remote sensing applications including microwave NDE. Neural network modules were designed to accomplish the two main processing goals of recognizing the 'subsurface pattern' followed by the identification of the depths of the subsurface layers like permafrost, groundwater table, and bedrock. We used an adaptive transform technique to transform raw GPR data into a small feature vector containing the most representative and discriminative features of the signal. This information formed the input for the neural network processing units. This strategy reduced the number of required training samples for the neural network by orders of magnitude. The entire processing system was trained using the adaptive transformed feature vector inputs and tested with real measured GPR data. The successful results of this system establishes the feasibility the feasibility of delineating subsurface layering nondestructively

Distribution network topology identification based on synchrophasor

Directory of Open Access Journals (Sweden)

Stefania Conti

2018-03-01

Full Text Available A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.

Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

Directory of Open Access Journals (Sweden)

Bo Fan

2014-01-01

Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

Geometrical methods for power network analysis

Energy Technology Data Exchange (ETDEWEB)

Bellucci, Stefano; Tiwari, Bhupendra Nath [Istituto Nazioneale di Fisica Nucleare, Frascati, Rome (Italy). Lab. Nazionali di Frascati; Gupta, Neeraj [Indian Institute of Technology, Kanpur (India). Dept. of Electrical Engineering

2013-02-01

Uses advanced geometrical methods to analyse power networks. Provides a self-contained and tutorial introduction. Includes a fully worked-out example for the IEEE 5 bus system. This book is a short introduction to power system planning and operation using advanced geometrical methods. The approach is based on well-known insights and techniques developed in theoretical physics in the context of Riemannian manifolds. The proof of principle and robustness of this approach is examined in the context of the IEEE 5 bus system. This work addresses applied mathematicians, theoretical physicists and power engineers interested in novel mathematical approaches to power network theory.

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

International Nuclear Information System (INIS)

Husam Fayiz, Al Masri

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms. (paper)

Autonomous target tracking of UAVs based on low-power neural network hardware

Science.gov (United States)

Yang, Wei; Jin, Zhanpeng; Thiem, Clare; Wysocki, Bryant; Shen, Dan; Chen, Genshe

2014-05-01

Detecting and identifying targets in unmanned aerial vehicle (UAV) images and videos have been challenging problems due to various types of image distortion. Moreover, the significantly high processing overhead of existing image/video processing techniques and the limited computing resources available on UAVs force most of the processing tasks to be performed by the ground control station (GCS) in an off-line manner. In order to achieve fast and autonomous target identification on UAVs, it is thus imperative to investigate novel processing paradigms that can fulfill the real-time processing requirements, while fitting the size, weight, and power (SWaP) constrained environment. In this paper, we present a new autonomous target identification approach on UAVs, leveraging the emerging neuromorphic hardware which is capable of massively parallel pattern recognition processing and demands only a limited level of power consumption. A proof-of-concept prototype was developed based on a micro-UAV platform (Parrot AR Drone) and the CogniMemTMneural network chip, for processing the video data acquired from a UAV camera on the y. The aim of this study was to demonstrate the feasibility and potential of incorporating emerging neuromorphic hardware into next-generation UAVs and their superior performance and power advantages towards the real-time, autonomous target tracking.

Investments in power networks and alternative measures

International Nuclear Information System (INIS)

2003-01-01

Measures taken with respect to production and consumption are often alternatives to investments in the power networks. While decisions about production and consumption are taken in the market, the network operation is subject to monopoly regulation. In the central network, Statnett's commission is to invest on the basis of socioeconomic profitability. There is a need for much better coordination between network investments and other measures in the power system. The price signal from the market and general tariffs are not sufficient to realize optimal solutions, and there is a need for a ''visible hand'' that can contribute to the realization of the solutions that are the best in each individual situation. It is desirable to create processes and incentives that realize the best solutions, independently of dealing with network investments, local power production or other measures.

Power laws and fragility in flow networks.

Science.gov (United States)

Shore, Jesse; Chu, Catherine J; Bianchi, Matt T

2013-01-01

What makes economic and ecological networks so unlike other highly skewed networks in their tendency toward turbulence and collapse? Here, we explore the consequences of a defining feature of these networks: their nodes are tied together by flow. We show that flow networks tend to the power law degree distribution (PLDD) due to a self-reinforcing process involving position within the global network structure, and thus present the first random graph model for PLDDs that does not depend on a rich-get-richer function of nodal degree. We also show that in contrast to non-flow networks, PLDD flow networks are dramatically more vulnerable to catastrophic failure than non-PLDD flow networks, a finding with potential explanatory power in our age of resource- and financial-interdependence and turbulence.

Modeling generator power plant portfolios and pollution taxes in electric power supply chain networks: a transportation network equilibrium transformation

International Nuclear Information System (INIS)

Kai Wu; Nagurney, A.; University of Massachusetts, Amherst, MA; Zugang Liu; Stranlund, J.K.

2006-01-01

Global climate change and fuel security risks have encouraged international and regional adoption of pollution/carbon taxes. A major portion of such policy interventions is directed at the electric power industry with taxes applied according to the type of fuel used by the power generators in their power plants. This paper proposes an electric power supply chain network model that captures the behavior of power generators faced with a portfolio of power plant options and subject to pollution taxes. We demonstrate that this general model can be reformulated as a transportation network equilibrium model with elastic demands and qualitatively analyzed and solved as such. The connections between these two different modeling schemas is done through finite-dimensional variational inequality theory. The numerical examples illustrate how changes in the pollution/carbon taxes affect the equilibrium electric power supply chain network production outputs, the transactions between the various decision-makers the demand market prices, as well as the total amount of carbon emissions generated. (author)

Power quality enhancement of renewable energy source power network using SMES system

International Nuclear Information System (INIS)

Seo, H.R.; Kim, A.R.; Park, M.; Yu, I.K.

2011-01-01

Power quality enhancement of a renewable energy source power network is performed by a real-toroidal-type SMES coil. SMES unit charges and discharges the HTS coil to mitigate the fluctuation of PV system output power. The grid connected PV and SMES system has been modeled and simulated using power-hard-in-the-loop simulation. The PHILS results demonstrated the effectiveness of the SMES system for enhancing power quality. This paper deals with power quality enhancement of renewable energy source power network using SMES system and describes the operation characteristics of HTS SMES system using real-toroidal-type SMES coil for smoothening the fluctuation of large-scale renewable energy source such as photovoltaic (PV) power generation system. It generates maximum power of PV array under various weather conditions. SMES unit charges and discharges the HTS coil to mitigate the fluctuation of PV system output power. The SMES unit is controlled according to the PV array output and the utility power quality conditions. The grid connected PV and SMES system has been modeled and simulated using power-hard-in-the-loop simulation (PHILS). The PHILS results demonstrated the effectiveness of the SMES system for enhancing power quality in power network including large-scale renewable energy source, especially PV power generation system.

Dynamic neural networks based on-line identification and control of high performance motor drives

Science.gov (United States)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

White blood cells identification system based on convolutional deep neural learning networks.

Science.gov (United States)

Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

2017-11-16

White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

Power consumption optimization strategy for wireless networks

DEFF Research Database (Denmark)

Cornean, Horia; Kumar, Sanjay; Marchetti, Nicola

2011-01-01

in order to reduce the total power consumption in a multi cellular network. We present an algorithm for power optimization under no interference and in presence of interference conditions, targeting to maximize the network capacity. The convergence of the algorithm is guaranteed if the interference...

Introduction to neural networks with electric power applications

International Nuclear Information System (INIS)

Wildberger, A.M.; Hickok, K.A.

1990-01-01

This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

Aspects concerning power distribution networks planning using artificial intelligence

Energy Technology Data Exchange (ETDEWEB)

Georgescu, Gh.; Gavrilas, M.; Cartina, Gh. [Gh. Asachi Technical Univ. of Iasi, Iasi (Romania)

1997-12-31

This paper presents the application of AI tools for the on-line identification of load structure in distribution networks. The authors have considered Artificial Neural Networks (ANN) which are known as valuable and fast tools for pattern identification or completion. This approach to the load model allows a more detailed analysis directed towards the optimization of system structure and working conditions. Traditional methods produce good results but raise the processing time problem, especially when applied to large systems. For such cases another approach appeal to the Genetic Algorithms, which are frequently referenced in the literature concerned with PDS (reconfiguration of open loop radial networks, optimal var-sources distribution, optimal selection of transformer tap position). (author)

A Gamma Memory Neural Network for System Identification

Science.gov (United States)

Motter, Mark A.; Principe, Jose C.

1992-01-01

A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

Power Aware Dynamic Provisioning of HPC Networks

Energy Technology Data Exchange (ETDEWEB)

Groves, Taylor [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Grant, Ryan [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-10-01

Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.

Identification of Complex Dynamical Systems with Neural Networks (2/2)

CERN Multimedia

CERN. Geneva

2016-01-01

The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

Identification of Complex Dynamical Systems with Neural Networks (1/2)

CERN Multimedia

CERN. Geneva

2016-01-01

The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

Identification of control targets in Boolean molecular network models via computational algebra.

Science.gov (United States)

Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

2016-09-23

Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

Comparing Different Fault Identification Algorithms in Distributed Power System

Science.gov (United States)

Alkaabi, Salim

A power system is a huge complex system that delivers the electrical power from the generation units to the consumers. As the demand for electrical power increases, distributed power generation was introduced to the power system. Faults may occur in the power system at any time in different locations. These faults cause a huge damage to the system as they might lead to full failure of the power system. Using distributed generation in the power system made it even harder to identify the location of the faults in the system. The main objective of this work is to test the different fault location identification algorithms while tested on a power system with the different amount of power injected using distributed generators. As faults may lead the system to full failure, this is an important area for research. In this thesis different fault location identification algorithms have been tested and compared while the different amount of power is injected from distributed generators. The algorithms were tested on IEEE 34 node test feeder using MATLAB and the results were compared to find when these algorithms might fail and the reliability of these methods.

NNSYSID and NNCTRL Tools for system identification and control with neural networks

DEFF Research Database (Denmark)

Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

2001-01-01

choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

NNSYSID and NNCTRL Tools for system identification and control with neural networks

DEFF Research Database (Denmark)

Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

2001-01-01

a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

Science.gov (United States)

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-01-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

Future view of electric power information processing techniques. Architecture techniques for power supply communication network

Energy Technology Data Exchange (ETDEWEB)

Tanaka, Keisuke

1988-06-20

Present situations of a power supply communication are described, and the future trend of a power supply information network is reviewed. For the improvement of a transmission efficiency and quality and a cost benefit for the power supply communication, the introduction of digital networks has been promoted. As for a protection information network, since there is the difference between a required communication quality of system protection information and that of power supply operation information, the individual digital network configuration is expected, in addition, the increasing of image information transmission for monitoring is also estimated. As for a business information network, the construction of a broad-band switched network is expected with increasing of image transmission needs such as a television meeting. Furthermore, the expansion to a power supply ISDN which is possible to connect between a telephone, facsimile and data terminal, to exchange various media and to connect between networks is expected with higher communication services in the protection and business network. However, for its practical use, the standardization of various interfaces will become essential. (3 figs, 1 tab)

Data identification for improving gene network inference using computational algebra.

Science.gov (United States)

Dimitrova, Elena; Stigler, Brandilyn

2014-11-01

Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

Maintaining the power balance in an "empty network"

NARCIS (Netherlands)

Reza, M.; Dominguez, A.O.; Schavemaker, P.H.; Kling, W.L.

2006-01-01

This paper presents the concept of an empty network and shows how the power balance can be maintained in such a system. In this study, an empty network is defined as a power system in which no rotating mass is present; all generators are grid-connected via power electronic interfaces. One generator

Power consumption in multicore fibre networks

DEFF Research Database (Denmark)

Nooruzzaman, Md; Jain, Saurabh; Jung, Yongmin

2017-01-01

We study potential energy savings in MCF-based networks compared to SMF-based ones in a Pan-European network topology based on the power consumption of recently fabricated cladding-pumped multi-core optical fibre amplifiers....

A modular structure to accident identification using neural networks

International Nuclear Information System (INIS)

Duque Estrada, Cassius Rodrigo

2005-01-01

This work uses the accident identification method based on Artificial Neural Networks (ANN) as basic blocks of a modular structure, allowing the inclusion of new accidents to be identified without modifying the ANN already trained. This structure comprises several modules for accident identification and one module for analysis. Each identification module follows the structure of the basic block. The identification modules are responsible for the recognition of an accident belonging to the specific set of events for which it were trained. The analysis module processes the output from the identification module to determine the system response. In order to test this structure it was proposed a transient identification problem comprising fifty accidents distributed in five identification modules. The results have demonstrated that the accident identification method used as basic block of a modular structure allows the inclusion of new sets of accidents, or variations of a same accident, without modifying the ANN already trained. For this, it is enough to include into the system an specific module for this new set of accidents. (author)

Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

Science.gov (United States)

Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

2011-05-01

A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

Directory of Open Access Journals (Sweden)

A. A. Doudkin

2015-01-01

Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

Power Control in Wireless Sensor Networks with Variable Interference

Directory of Open Access Journals (Sweden)

Michele Chincoli

2016-01-01

Full Text Available Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches.

Adaptive intelligent power systems: Active distribution networks

International Nuclear Information System (INIS)

McDonald, Jim

2008-01-01

Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems

Topology Identification of General Dynamical Network with Distributed Time Delays

International Nuclear Information System (INIS)

Zhao-Yan, Wu; Xin-Chu, Fu

2009-01-01

General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)

Boosted jet identification using particle candidates and deep neural networks

CERN Document Server

CMS Collaboration

2017-01-01

This note presents developments for the identification of hadronically decaying top quarks using deep neural networks in CMS. A new method that utilizes one dimensional convolutional neural networks based on jet constituent particles is proposed. Alternative methods using boosted decision trees based on jet observables are compared. The new method shows significant improvement in performance.

Reactive power management of power networks with wind generation

CERN Document Server

Amaris, Hortensia; Ortega, Carlos Alvarez

2012-01-01

As the energy sector shifts and changes to focus on renewable technologies, the optimization of wind power becomes a key practical issue. Reactive Power Management of Power Networks with Wind Generation brings into focus the development and application of advanced optimization techniques to the study, characterization, and assessment of voltage stability in power systems. Recent advances on reactive power management are reviewed with particular emphasis on the analysis and control of wind energy conversion systems and FACTS devices. Following an introduction, distinct chapters cover the 5 key

Some general rules governing huge power networks

International Nuclear Information System (INIS)

Clade, J.

2010-01-01

The very large networks operate on vast territories, on the scale not only of countries, but of continents. Their aim is twofold: transmitting electrical energy from the power plants - nuclear or thermal power plants, hydraulic, wind power plants etc. - to consumption areas (that is the transmission function); pooling the power plants, so as to operate at any time those which are the less expensive (interconnection of energy production) and to guarantee a safe continuous supply to the users (interconnection of power). However, the transmission of electrical energy is more costly than transmission, when possible, of primary energy sources, either fossil fuels or nuclear. When the sources are not chiefly hydraulic, it is pertinent to limit the transmission of electricity by siting the power plants as close as possible to the consumption areas. On the contrary, interconnection may allow significant savings in the way of power plant investments and fuel expenses. Therein is the main economical justification for very large electrical systems and networks, except in cases where distant hydraulic sources are operated. We must then think over large electrical networks mainly as tools for cooperation between power producers, aiming at an electrical supply to customers which is safe, continuous and as inexpensive as possible. (author)

Research on Holographic Evaluation of Service Quality in Power Data Network

Science.gov (United States)

Wei, Chen; Jing, Tao; Ji, Yutong

2018-01-01

With the rapid development of power data network, the continuous development of the Power data application service system, more and more service systems are being put into operation. Following this, the higher requirements for network quality and service quality are raised, in the actual process for the network operation and maintenance. This paper describes the electricity network and data network services status. A holographic assessment model was presented to achieve a comprehensive intelligence assessment on the power data network and quality of service in the operation and maintenance on the power data network. This evaluation method avoids the problems caused by traditional means which performs a single assessment of network performance quality. This intelligent Evaluation method can improve the efficiency of network operation and maintenance guarantee the quality of real-time service in the power data network..

A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

Energy Technology Data Exchange (ETDEWEB)

Acernese, F [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Barone, F [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, M de [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Rosa, R De [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Eleuteri, A [Istituto Nazionale di Fisica Nucleare, sez. Napoli, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Milano, L [Dipartimento di Scienze Fisiche, Universita di Napoli Federico II, Complesso Universitario di Monte S Angelo, via Cintia, I-80126 Naples (Italy); Tagliaferri, R [Dipartimento di Matematica ed Informatica, Universita di Salerno, via S Allende, I-84081 Baronissi (Salerno) (Italy)

2002-06-21

In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

International Nuclear Information System (INIS)

Acernese, F; Barone, F; Rosa, M de; Rosa, R De; Eleuteri, A; Milano, L; Tagliaferri, R

2002-01-01

In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis

A neural network-based approach to noise identification of interferometric GW antennas: the case of the 40 m Caltech laser interferometer

CERN Document Server

Acernese, F; Rosa, M D; Rosa, R D; Eleuteri, A; Milano, L; Tagliaferri, R

2002-01-01

In this paper, a neural network-based approach is presented for the real time noise identification of a GW laser interferometric antenna. The 40 m Caltech laser interferometer output data provide a realistic test bed for noise identification algorithms because of the presence of many relevant effects: violin resonances in the suspensions, main power harmonics, ring-down noise from servo control systems, electronic noises, glitches and so on. These effects can be assumed to be present in all the first interferometric long baseline GW antennas such as VIRGO, LIGO, GEO and TAMA. For noise identification, we used the Caltech-40 m laser interferometer data. The results we obtained are pretty good notwithstanding the high initial computational cost. The algorithm we propose is general and robust, taking into account that it does not require a priori information on the data, nor a precise model, and it constitutes a powerful tool for time series data analysis.

Designing reliable wireless sensor network for nuclear power plant

International Nuclear Information System (INIS)

Fujiwara, Takeshi; Takahashi, Hiroyuki

2007-01-01

This study proposes an innovative method for the monitoring the nuclear power plant. In this field, false detection of the trouble, both 'false negative' and 'false positive' will become a serious problem. In the other hand, since nuclear power plant is such a complicated system, wireless is required for implementing into real field. Considering these backgrounds, we propose a new reliable health monitoring system for nuclear power plant. This is based on an idea, 'a network on a network', such as 'wireless global network' on 'local network with self-maintenance function.' (author)

Research on spot power market equilibrium model considering the electric power network characteristics

International Nuclear Information System (INIS)

Wang, Chengmin; Jiang, Chuanwen; Chen, Qiming

2007-01-01

Equilibrium is the optimum operational condition for the power market by economics rule. A realistic spot power market cannot achieve the equilibrium condition due to network losses and congestions. The impact of the network losses and congestion on spot power market is analyzed in this paper in order to establish a new equilibrium model considering the network loss and transmission constraints. The OPF problem formulated according to the new equilibrium model is solved by means of the equal price principle. A case study on the IEEE-30-bus system is provided in order to prove the effectiveness of the proposed approach. (author)

Wind power prediction based on genetic neural network

Science.gov (United States)

Zhang, Suhan

2017-04-01

The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

A port-Hamiltonian approach to power network modeling and analysis

NARCIS (Netherlands)

Fiaz, S.; Zonetti, D.; Ortega, R.; Scherpen, J.M.A.; van der Schaft, A.J.

2013-01-01

In this paper we present a systematic framework for modeling of power networks. The basic idea is to view the complete power network as a port-Hamiltonian system on a graph where edges correspond to components of the power network and nodes are buses. The interconnection constraints are given by the

Network position and related power : how they affect and are affected by network management and outcomes

NARCIS (Netherlands)

Oukes, Tamara

2018-01-01

In network position and related power you learn more about how network position and related power affect and are affected by network management and outcomes. First, I expand our present understanding of how startups with a fragile network position manage business relationships by taking an

Efficiency of Finish power transmission network companies

International Nuclear Information System (INIS)

Anon.

2001-01-01

The Finnish Energy Market Authority has investigated the efficiency of power transmissions network companies. The results show that the intensification potential of the branch is 402 million FIM, corresponding to about 15% of the total costs of the branch and 7.3 % of the turnout. Energy Market Authority supervises the reasonableness of the power transmission prices, and it will use the results of the research in supervision. The research was carried out by the Quantitative Methods Research Group of Helsinki School of Economics. The main objective of the research was to create an efficiency estimation method for electric power distribution network business used for Finnish conditions. Data of the year 1998 was used as basic material in the research. Twenty-one of the 102 power distribution network operators was estimated to be totally efficient. Highest possible efficiency rate was 100, and the average of the efficiency rates of all the operators was 76.9, the minimum being 42.6

Statistical Power in Longitudinal Network Studies

NARCIS (Netherlands)

Stadtfeld, Christoph; Snijders, Tom A. B.; Steglich, Christian; van Duijn, Marijtje

2018-01-01

Longitudinal social network studies may easily suffer from a lack of statistical power. This is the case in particular for studies that simultaneously investigate change of network ties and change of nodal attributes. Such selection and influence studies have become increasingly popular due to the

Power plant fault detection using artificial neural network

Science.gov (United States)

Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

2018-02-01

The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

The NNSYSID Toolbox - A MATLAB Toolbox for System Identification with Neural Networks

DEFF Research Database (Denmark)

Nørgård, Peter Magnus; Ravn, Ole; Hansen, Lars Kai

1996-01-01

To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determi......To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms...

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

The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

Energy Technology Data Exchange (ETDEWEB)

Psihas, Fernanda [Indiana U.

2017-11-22

In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

User Identification Framework in Social Network Services Environment

Directory of Open Access Journals (Sweden)

Brijesh BAKARIYA

2014-01-01

Full Text Available Social Network Service is a one of the service where people may communicate with one an-other; and may also exchange messages even of any type of audio or video communication. Social Network Service as name suggests a type of network. Such type of web application plays a dominant role in internet technology. In such type of online community, people may share their common interest. Facebook LinkedIn, orkut and many more are the Social Network Service and it is good medium of making link with people having unique or common interest and goals. But the problem of privacy protection is a big issue in today’s world. As social networking sites allows anonymous users to share information of other stuffs. Due to which cybercrime is also increasing to a rapid extent. In this article we preprocessed the web log data of Social Network Services and assemble that data on the basis of image file format like jpg, jpeg, gif, png, bmp etc. and also propose a framework for victim’s identification.

Dense power-law networks and simplicial complexes

Science.gov (United States)

Courtney, Owen T.; Bianconi, Ginestra

2018-05-01

There is increasing evidence that dense networks occur in on-line social networks, recommendation networks and in the brain. In addition to being dense, these networks are often also scale-free, i.e., their degree distributions follow P (k ) ∝k-γ with γ ∈(1 ,2 ] . Models of growing networks have been successfully employed to produce scale-free networks using preferential attachment, however these models can only produce sparse networks as the numbers of links and nodes being added at each time step is constant. Here we present a modeling framework which produces networks that are both dense and scale-free. The mechanism by which the networks grow in this model is based on the Pitman-Yor process. Variations on the model are able to produce undirected scale-free networks with exponent γ =2 or directed networks with power-law out-degree distribution with tunable exponent γ ∈(1 ,2 ) . We also extend the model to that of directed two-dimensional simplicial complexes. Simplicial complexes are generalization of networks that can encode the many body interactions between the parts of a complex system and as such are becoming increasingly popular to characterize different data sets ranging from social interacting systems to the brain. Our model produces dense directed simplicial complexes with power-law distribution of the generalized out-degrees of the nodes.

Power control algorithms for mobile ad hoc networks

Directory of Open Access Journals (Sweden)

Nuraj L. Pradhan

2011-07-01

We will also focus on an adaptive distributed power management (DISPOW algorithm as an example of the multi-parameter optimization approach which manages the transmit power of nodes in a wireless ad hoc network to preserve network connectivity and cooperatively reduce interference. We will show that the algorithm in a distributed manner builds a unique stable network topology tailored to its surrounding node density and propagation environment over random topologies in a dynamic mobile wireless channel.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks

Directory of Open Access Journals (Sweden)

Chemmangattuvalappil Nishanth

2012-09-01

Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

Rapid identification of sequences for orphan enzymes to power accurate protein annotation.

Directory of Open Access Journals (Sweden)

Kevin R Ramkissoon

Full Text Available The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the "back catalog" of enzymology--"orphan enzymes," those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC database alone. In this study, we demonstrate how this orphan enzyme "back catalog" is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology's "back catalog" another powerful tool to drive accurate genome annotation.

Rapid Identification of Sequences for Orphan Enzymes to Power Accurate Protein Annotation

Science.gov (United States)

Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the “back catalog” of enzymology – “orphan enzymes,” those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme “back catalog” is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology’s “back catalog” another powerful tool to drive accurate genome annotation. PMID:24386392

Wide-band segmented power distribution networks

NARCIS (Netherlands)

Tereshchenko, O.V.; Buesink, Frederik Johannes Karel; Leferink, Frank Bernardus Johannes

2013-01-01

This paper discusses a novel design of Power Distribution Network (PDN). By physical structuring of the power plane into repetitive symmetrical and asymmetrical segments of varying size, suppression of the propagation of unwanted noise throughout the PDN over a wide frequency range is achieved.

Observability in electric power networks: identification critical measures methods; Observabilidade em redes de energia eletrica: metodos de identificacao de medidas criticas

Energy Technology Data Exchange (ETDEWEB)

London Junior, Joao Bosco Augusto

1997-07-01

One of the most important functions of the control and operation centers is to maintain service reliability in a electrical power system. In order to obtain a reliable operation of the power system, it is important to identify the critical measurements, and then to improve the measurement system using pseudo measurements. The goal of this work is to determine more efficient methods for critical measurement identification. A brief review of the some methods for observability analysis as well as two methodologies to identify critical measurements are presented. The first method has a combinatorial nature; the second one is supported by uni modular M matrix (incidence matrix of measurements for branches) and A matrix (incidence matrix of branch for nodes). The second method needs a combinatorial algorithm to be feasible, so that it becomes a slow method. Two new methods for critical measurements identification are presented in this work: the first one is based on the theory developed by Bretas (1996a), to analyse observability using graph paths; the second methods is supported y the Slutsker and Scudder (1987) theory, where identification is reached throughout the analysis of the jacobian matrix. (author)

System Identification and Resonant Control of Thermoacoustic Engines for Robust Solar Power

Directory of Open Access Journals (Sweden)

Boe-Shong Hong

2015-05-01

Full Text Available It was found that thermoacoustic solar-power generators with resonant control are more powerful than passive ones. To continue the work, this paper focuses on the synthesis of robustly resonant controllers that guarantee single-mode resonance not only in steady states, but also in transient states when modelling uncertainties happen and working temperature temporally varies. Here the control synthesis is based on the loop shifting and the frequency-domain identification in advance thereof. Frequency-domain identification is performed to modify the mathematical modelling and to identify the most powerful mode, so that the DSP-based feedback controller can online pitch the engine to the most powerful resonant-frequency robustly and accurately. Moreover, this paper develops two control tools, the higher-order van-der-Pol oscillator and the principle of Dynamical Equilibrium, to assist in system identification and feedback synthesis, respectively.

Electronic Power Transformer for Power Distribution Networks

Directory of Open Access Journals (Sweden)

Ermuraсhi Iu.V.

2017-12-01

Full Text Available Reducing losses in electricity distribution networks is a current technical problem. This issue also has social and environmental aspects. As a promising solution one can examine the direct distribution from the medium voltage power network using new equipment based on the use of power electronics. The aim of the paper is to propose and argue an innovative technical solution for the realization of the Solid State Transformer (SST in order to decrease the number of energy transformation stages compared to the known solutions, simplifying the topology of the functional scheme with the reduction of production costs and the loss of energy in transformers used in electrical distribution networks. It is proposed the solution of simplifying the topology of the AC/AC electronic transformer by reducing the number of passive electronic components (resistors, inductors, capacitors and active (transistors. The inverter of the SST transformer ensures the switching mode of the transistors, using for this purpose the inductance of the magnetic leakage flux of the high frequency transformer. The robustness of the laboratory sample of the SST 10 / 0.22 kV transformer with the power of 20 kW was manufactured and tested. Testing of the laboratory sample confirmed the functionality of the proposed scheme and the possibility of switching of the transistors to at zero current (ZCS mode with the reduction of the energy losses. In the proposed converter a single high-frequency transformer with a simplified construction with two windings is used, which reduces its mass and the cost of making the transformer. The reduction in the manufacturing cost of the converter is also due to the decrease in the number of links between the functional elements.

Tactical Network Load Balancing in Multi-Gateway Wireless Sensor Networks

Science.gov (United States)

2013-12-01

communication technology ARPANET Advanced Research Projects Agency Network ASN autonomous sensor network CBR constant bit rate CDMA code...transmission energy NFC near field communication OV1 operational view xxii PA power amplifier RFC request for comment RFID radio frequency identification...fact that the integrated chip (IC) technology boom during the past 20+ years has miniaturized IC hardware while increasing computational capability

Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

DEFF Research Database (Denmark)

Wan, Can; Song, Yonghua; Xu, Zhao

2016-01-01

probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

Complexity of Resilient Power Distribution Networks

International Nuclear Information System (INIS)

May, Michael

2008-01-01

Power Systems in general and specifically the problems of communication, control, and coordination in human supervisory control of electric power transmission and distribution networks constitute a good case study for resilience engineering. Because of the high cost and high impact on society of transmission disturbances and blackouts and the vulnerability of power networks to terrorist attacks, Transmission Systems Operators (TSOs) are already focusing on organizational structures, procedures, and technical innovations that could improve the flexibility and robustness of power Systems and achieve the overall goal of providing secure power supply. For a number of reasons however the complexity of power Systems is increasing and new problems arise for human supervisory control and the ability of these Systems to implement fast recovery from disturbances. Around the world power Systems are currently being restructured to adapt to regional electricity markets and secure the availability, resilience and sustainability of electric power generation, transmission and distribution. This demands a reconsideration of the available decision support, the activity of human supervisory control of the highly automated processes involved and the procedures regulating it, as well as the role of the TSOs and the regional, national and international organizations set up to manage their activity. Unfortunately we can expect that human supervisory control of power Systems will become more complex in the near future for a number of reasons. The European Union for the Co-ordination of Transmission of Electricity (UCTE) has remarked that although the interconnected Systems of power transmission networks has been developed over the years with the main goal of providing secure power supply through common use of reserve capacities and the optimization of the use of energy resources, today's market dynamics imposing a high level of cross-border exchanges is 'out of the scope of the

Load power device, system and method of load control and management employing load identification

Science.gov (United States)

Yang, Yi; Luebke, Charles John; Schoepf, Thomas J.

2018-01-09

A load power device includes a power input, at least one power output for at least one load, a plurality of sensors structured to sense voltage and current at the at least one power output, and a processor. The processor provides: (a) load identification based upon the sensed voltage and current, and (b) load control and management based upon the load identification.

Modeling geomagnetic induced currents in Australian power networks

Science.gov (United States)

Marshall, R. A.; Kelly, A.; Van Der Walt, T.; Honecker, A.; Ong, C.; Mikkelsen, D.; Spierings, A.; Ivanovich, G.; Yoshikawa, A.

2017-07-01

Geomagnetic induced currents (GICs) have been considered an issue for high-latitude power networks for some decades. More recently, GICs have been observed and studied in power networks located in lower latitude regions. This paper presents the results of a model aimed at predicting and understanding the impact of geomagnetic storms on power networks in Australia, with particular focus on the Queensland and Tasmanian networks. The model incorporates a "geoelectric field" determined using a plane wave magnetic field incident on a uniform conducting Earth, and the network model developed by Lehtinen and Pirjola (1985). Model results for two intense geomagnetic storms of solar cycle 24 are compared with transformer neutral monitors at three locations within the Queensland network and one location within the Tasmanian network. The model is then used to assess the impacts of the superintense geomagnetic storm of 29-31 October 2003 on the flow of GICs within these networks. The model results show good correlation with the observations with coefficients ranging from 0.73 to 0.96 across the observing sites. For Queensland, modeled GIC magnitudes during the superstorm of 29-31 October 2003 exceed 40 A with the larger GICs occurring in the south-east section of the network. Modeled GICs in Tasmania for the same storm do not exceed 30 A. The larger distance spans and general east-west alignment of the southern section of the Queensland network, in conjunction with some relatively low branch resistance values, result in larger modeled GICs despite Queensland being a lower latitude network than Tasmania.

Synapse:neural network for predict power consumption: users guide

Energy Technology Data Exchange (ETDEWEB)

Muller, C; Mangeas, M; Perrot, N

1994-08-01

SYNAPSE is forecasting tool designed to predict power consumption in metropolitan France on the half hour time scale. Some characteristics distinguish this forecasting model from those which already exist. In particular, it is composed of numerous neural networks. The idea for using many neural networks arises from past tests. These tests showed us that a single neural network is not able to solve the problem correctly. From this result, we decided to perform unsupervised classification of the 24 consumption curves. From this classification, six classes appeared, linked with the weekdays: Mondays, Tuesdays, Wednesdays, Thursdays, Fridays, Saturdays, Sundays, holidays and bridge days. For each class and for each half hour, two multilayer perceptrons are built. The two of them forecast the power for one particular half hour, and for a day including one of the determined class. The input of these two network are different: the first one (short time forecasting) includes the powers for the most recent half hour and relative power of the previous day; the second (medium time forecasting) includes only the relative power of the previous day. A process connects the results of every networks and allows one to forecast more than one half-hour in advance. In this process, short time forecasting networks and medium time forecasting networks are used differently. The first kind of neural networks gives good results on the scale of one day. The second one gives good forecasts for the next predicted powers. In this note, the organization of the SYNAPSE program is detailed, and the user`s menu is described. This first version of synapse works and should allow the APC group to evaluate its utility. (authors). 6 refs., 2 appends.

Energy parameter estimation in solar powered wireless sensor networks

KAUST Repository

Mousa, Mustafa; Claudel, Christian G.

2014-01-01

The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.

Energy parameter estimation in solar powered wireless sensor networks

KAUST Repository

Mousa, Mustafa

2014-02-24

The operation of solar powered wireless sensor networks is associated with numerous challenges. One of the main challenges is the high variability of solar power input and battery capacity, due to factors such as weather, humidity, dust and temperature. In this article, we propose a set of tools that can be implemented onboard high power wireless sensor networks to estimate the battery condition and capacity as well as solar power availability. These parameters are very important to optimize sensing and communications operations and maximize the reliability of the complete system. Experimental results show that the performance of typical Lithium Ion batteries severely degrades outdoors in a matter of weeks or months, and that the availability of solar energy in an urban solar powered wireless sensor network is highly variable, which underlines the need for such power and energy estimation algorithms.

Impedance-Source Networks for Electric Power Conversion Part II

DEFF Research Database (Denmark)

Siwakoti, Yam P.; Peng, Fang Zheng; Blaabjerg, Frede

2015-01-01

Impedance-source networks cover the entire spectrum of electric power conversion applications (dc-dc, dc-ac, ac-dc, ac-ac) controlled and modulated by different modulation strategies to generate the desired dc or ac voltage and current at the output. A comprehensive review of various impedance......-source-network-based power converters has been covered in a previous paper and main topologies were discussed from an application point of view. Now Part II provides a comprehensive review of the most popular control and modulation strategies for impedance-source network-based power converters/inverters. These methods...

Power control in wireless sensor networks with variable interference

NARCIS (Netherlands)

Chincoli, M.; Syed, A.A.; Exarchakos, G.; Liotta, A.

2016-01-01

Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive

Group handoff management in low power microcell-femtocell network

Directory of Open Access Journals (Sweden)

Debashis De

2017-02-01

Full Text Available This paper presents an analytical model of group based hand-off management based on bird flocking behavior. In the proposed scheme, a number of mobile devices form a group if these devices move together for a long time duration. Although call delivery or call generation are performed individually, hand-off is performed in a group. Dynamic group formation, group division and group merging methods are proposed in this paper. From the simulation results it is demonstrated that approximately 75%, 65% and 90% reduction in power, cost and latency consumption can be obtained respectively using group hand-off management. Thus the proposed scheme is referred as green, economic and fast hand-off strategy. In this paper instead of a macrocell network, a microcell-femtocell network is considered as the transmission power of a microcell or a femtocell base station is much less than a macrocell base station. Simulation results present that the microcell-femtocell network achieves approximately 25–55% and 35–55% reduction in power transmission, and 50–65% and 15–45% reduction in path loss than only a macrocell network and macrocell-femtocell network respectively. Thus microcell-femtocell network is a power-efficient network.

Real-time identification of vehicle motion-modes using neural networks

Science.gov (United States)

Wang, Lifu; Zhang, Nong; Du, Haiping

2015-01-01

A four-wheel ground vehicle has three body-dominated motion-modes, that is, bounce, roll, and pitch motion-modes. Real-time identification of these motion-modes can make vehicle suspensions, in particular, active suspensions, target on the dominant motion-mode and apply appropriate control strategies to improve its performance with less power consumption. Recently, a motion-mode energy method (MEM) was developed to identify the vehicle body motion-modes. However, this method requires the measurement of full vehicle states and road inputs, which are not always available in practice. This paper proposes an alternative approach to identify vehicle primary motion-modes with acceptable accuracy by employing neural networks (NNs). The effectiveness of the trained NNs is verified on a 10-DOF full-car model under various types of excitation inputs. The results confirm that the proposed method is effective in determining vehicle primary motion-modes with comparable accuracy to the MEM method. Experimental data is further used to validate the proposed method.

Identification of crystalline structures using Moessbauer parameters and artificial neural network

International Nuclear Information System (INIS)

Salles, E.O.T.; Souza Junior, P.A. De; Garg, V.K.

1995-01-01

Moessbauer spectroscopy is a useful technique for characterizing the valences, electronic and magnetic states, coordination symmetric and site occupancies of Fe cations. The Moessbauer parameters of Isomer Shift (I.S.) and Quadrupole Splitting (Q.S.) are useful to distinguish paramagnetic ferrous and ferric ions in several substances, while the internal magnetic field provides information on the crystallinity. A correlation is being sought between Moessbauer parameters and several structure properties of some iron-containing minerals using Artificial Neural Networks (ANN). Distinct regions of crystalline structures are defined when any two parameters are plotted, but in several cases superposition of these regions leads to erroneous conclusions. We have tried to eliminate this difficulty by using convenient axes. These axes form n-dimensional vectors as input to our ANN. In recent years ANN has shown to be a powerful technique to solve problems as pattern recognition, optimization, preview ups and downs in stock market, automatic control and identification of a mineral from a Moessbauer spectrum of Moessbauer data bank. Using ANN we have been successful in identification of crystalline structures from plots of Moessbauer spectral parameters of I.S., Q.S., and structure using Moessbauer parameters of I.S., Q.S., and polyhedral volume of a coordination site are presented. (author) 28 refs.; 4 figs.; 2 tabs

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

International Nuclear Information System (INIS)

Canedo Medeiros, Jose Antonio Carlos; Schirru, Roberto

2008-01-01

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results

Identification of nuclear power plant transients using the Particle Swarm Optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Canedo Medeiros, Jose Antonio Carlos [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE, UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br

2008-04-15

In order to help nuclear power plant operator reduce his cognitive load and increase his available time to maintain the plant operating in a safe condition, transient identification systems have been devised to help operators identify possible plant transients and take fast and right corrective actions in due time. In the design of classification systems for identification of nuclear power plants transients, several artificial intelligence techniques, involving expert systems, neuro-fuzzy and genetic algorithms have been used. In this work we explore the ability of the Particle Swarm Optimization algorithm (PSO) as a tool for optimizing a distance-based discrimination transient classification method, giving also an innovative solution for searching the best set of prototypes for identification of transients. The Particle Swarm Optimization algorithm was successfully applied to the optimization of a nuclear power plant transient identification problem. Comparing the PSO to similar methods found in literature it has shown better results.

Autonomous Power Control MAC Protocol for Mobile Ad Hoc Networks

Directory of Open Access Journals (Sweden)

2006-01-01

Full Text Available Battery energy limitation has become a performance bottleneck for mobile ad hoc networks. IEEE 802.11 has been adopted as the current standard MAC protocol for ad hoc networks. However, it was developed without considering energy efficiency. To solve this problem, many modifications on IEEE 802.11 to incorporate power control have been proposed in the literature. The main idea of these power control schemes is to use a maximum possible power level for transmitting RTS/CTS and the lowest acceptable power for sending DATA/ACK. However, these schemes may degrade network throughput and reduce the overall energy efficiency of the network. This paper proposes autonomous power control MAC protocol (APCMP, which allows mobile nodes dynamically adjusting power level for transmitting DATA/ACK according to the distances between the transmitter and its neighbors. In addition, the power level for transmitting RTS/CTS is also adjustable according to the power level for DATA/ACK packets. In this paper, the performance of APCMP protocol is evaluated by simulation and is compared with that of other protocols.

Power networks in the heart of industrial civilization

International Nuclear Information System (INIS)

Bouneau, Ch.; Derdevet, M.; Percebois, J.

2007-01-01

Since more than a century, power networks have largely contributed to the economic development of our societies, to the improvement of our life conditions, and to the relations between communities. The 19. and 20. centuries have seen the multiplying and spreading of power networks over Europe, and at the beginning of the 21. century, it has become vital to improve and reinforce them. This book proposes to rediscover the main innovations of the 20. century and the men who participated to this energy revolution. It examines also the choices to be made in the months to come both in France and in Europe. The importance of a European energy policy, necessary for a development of power networks, is emphasized. At a time where each French end-user can chose his power supplier, this book allows to better understand the economic, social and political challenges of a real European solidarity. (J.S.)

Boosted decision trees as an alternative to artificial neural networks for particle identification

International Nuclear Information System (INIS)

Roe, Byron P.; Yang Haijun; Zhu Ji; Liu Yong; Stancu, Ion; McGregor, Gordon

2005-01-01

The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics

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.

A Multilayer Perceptron-Based Impulsive Noise Detector with Application to Power-Line-Based Sensor Networks

KAUST Repository

Chien, Ying-Ren

2018-04-10

For power-line-based sensor networks, impulsive noise (IN) will dramatically degrade the data transmission rate in the power line. In this paper, we present a multilayer perceptron (MLP)-based approach to detect IN in orthogonal frequency-division multiplexing (OFDM)-based baseband power line communications (PLCs). Combining the MLP-based IN detection method with the outlier detection theory allows more accurate identification of the harmful residual IN. For OFDM-based PLC systems, the high peak-to-average power ratio (PAPR) of the received signal makes detection of harmful residual IN more challenging. The detection mechanism works in an iterative receiver that contains a pre-IN mitigation and a post-IN mitigation. The pre-IN mitigation is meant to null the stronger portion of IN, while the post-IN mitigation suppresses the residual portion of IN using an iterative process. Compared with previously reported IN detectors, the simulation results show that our MLP-based IN detector improves the resulting bit error rate (BER) performance.

Impact of network topology on synchrony of oscillatory power grids

Energy Technology Data Exchange (ETDEWEB)

Rohden, Martin; Sorge, Andreas; Witthaut, Dirk [Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen (Germany); Timme, Marc [Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen (Germany); Faculty of Physics, Georg August Universität Göttingen, Göttingen (Germany)

2014-03-15

Replacing conventional power sources by renewable sources in current power grids drastically alters their structure and functionality. In particular, power generation in the resulting grid will be far more decentralized, with a distinctly different topology. Here, we analyze the impact of grid topologies on spontaneous synchronization, considering regular, random, and small-world topologies and focusing on the influence of decentralization. We model the consumers and sources of the power grid as second order oscillators. First, we analyze the global dynamics of the simplest non-trivial (two-node) network that exhibit a synchronous (normal operation) state, a limit cycle (power outage), and coexistence of both. Second, we estimate stability thresholds for the collective dynamics of small network motifs, in particular, star-like networks and regular grid motifs. For larger networks, we numerically investigate decentralization scenarios finding that decentralization itself may support power grids in exhibiting a stable state for lower transmission line capacities. Decentralization may thus be beneficial for power grids, regardless of the details of their resulting topology. Regular grids show a specific sharper transition not found for random or small-world grids.

Variable-Width Datapath for On-Chip Network Static Power Reduction

Energy Technology Data Exchange (ETDEWEB)

Michelogiannakis, George; Shalf, John

2013-11-13

With the tight power budgets in modern large-scale chips and the unpredictability of application traffic, on-chip network designers are faced with the dilemma of designing for worst- case bandwidth demands and incurring high static power overheads, or designing for an average traffic pattern and risk degrading performance. This paper proposes adaptive bandwidth networks (ABNs) which divide channels and switches into lanes such that the network provides just the bandwidth necessary in each hop. ABNs also activate input virtual channels (VCs) individually and take advantage of drowsy SRAM cells to eliminate false VC activations. In addition, ABNs readily apply to silicon defect tolerance with just the extra cost for detecting faults. For application traffic, ABNs reduce total power consumption by an average of 45percent with comparable performance compared to single-lane power-gated networks, and 33percent compared to multi-network designs.

Topological derivatives of eigenvalues and neural networks in identification of imperfections

International Nuclear Information System (INIS)

Grzanek, M; Nowakowski, A; Sokolowski, J

2008-01-01

Numerical method for identification of imperfections is devised for elliptic spectral problems. The neural networks are employed for numerical solution. The topological derivatives of eigenvalues are used in the learning procedure of the neural networks. The topological derivatives of eigenvalues are determined by the methods of asymptotic analysis in singularly perturbed geometrical domains. The convergence of the numerical method in a probabilistic setting is analysed. The method is presented for the identification of small singular perturbations of the boundary of geometrical domain, however the framework is general and can be used for numerical solutions of inverse problems in the presence of small imperfections in the interior of the domain. Some numerical results are given for elliptic spectral problem in two spatial dimensions.

Too much power to the networks

Directory of Open Access Journals (Sweden)

Alessandro Delfanti

2009-10-01

Full Text Available In his latest book titled “Communication power”, the famous sociologist of information society Manuel Castells focuses on the way in which power takes shape and acts in information societies, and the role of communication in defining, structuring, and changing it. From the rise of “mass self-communication” to the role of environmental movements and neuropolitics, the network is the key structure at play and the main lens used to analyse the transformations we are witnessing. To support his thesis Castells links media studies, power theory and brain science, but his insistence on networks puts in danger his ability to give to his readers a comprehensive and coherent interpretative framework.

SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

Directory of Open Access Journals (Sweden)

B. Jency Paulin

2016-01-01

Full Text Available Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP and training procedure for neural network is done by error Back Propagation (BP. In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted.

Identification of serial number on bank card using recurrent neural network

Science.gov (United States)

Liu, Li; Huang, Linlin; Xue, Jian

2018-04-01

Identification of serial number on bank card has many applications. Due to the different number printing mode, complex background, distortion in shape, etc., it is quite challenging to achieve high identification accuracy. In this paper, we propose a method using Normalization-Cooperated Gradient Feature (NCGF) and Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM) for serial number identification. The NCGF maps the gradient direction elements of original image to direction planes such that the RNN with direction planes as input can recognize numbers more accurately. Taking the advantages of NCGF and RNN, we get 90%digit string recognition accuracy.

Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

Science.gov (United States)

Liang, B.; Iwnicki, S. D.; Zhao, Y.

2013-08-01

The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

Impedance-Source Networks for Electric Power Conversion Part I

DEFF Research Database (Denmark)

Siwakoti, Yam P.; Peng, Fang Zheng; Blaabjerg, Frede

2015-01-01

power chain, which may improve the reliability and performance of the power system. The first part of this paper provides a comprehensive review of the various impedance-source-networks-based power converters and discusses the main topologies from an application point of view. This review paper...... is the first of its kind with the aim of providing a “one-stop” information source and a selection guide on impedance-source networks for power conversion for researchers, designers, and application engineers. A comprehensive review of various modeling, control, and modulation techniques for the impedance...

Self-Learning Power Control in Wireless Sensor Networks.

Science.gov (United States)

Chincoli, Michele; Liotta, Antonio

2018-01-27

Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This aggravates the already over-congested unlicensed radio bands, calling for new mechanisms to improve spectrum management and energy efficiency, such as transmission power control. Existing protocols are based on simplistic heuristics that often approach interference problems (i.e., packet loss, delay and energy waste) by increasing power, leading to detrimental results. The scope of this work is to investigate how machine learning may be used to bring wireless nodes to the lowest possible transmission power level and, in turn, to respect the quality requirements of the overall network. Lowering transmission power has benefits in terms of both energy consumption and interference. We propose a protocol of transmission power control through a reinforcement learning process that we have set in a multi-agent system. The agents are independent learners using the same exploration strategy and reward structure, leading to an overall cooperative network. The simulation results show that the system converges to an equilibrium where each node transmits at the minimum power while respecting high packet reception ratio constraints. Consequently, the system benefits from low energy consumption and packet delay.

Topology identification of the complex networks with non-delayed and delayed coupling

International Nuclear Information System (INIS)

Guo Wanli; Chen Shihua; Sun Wen

2009-01-01

In practical situation, there exists many uncertain information in complex networks, such as the topological structures. So the topology identification is an important issue in the research of the complex networks. Based on LaSalle's invariance principle, in this Letter, an adaptive controlling method is proposed to identify the topology of a weighted general complex network model with non-delayed and delayed coupling. Finally, simulation results show that the method is effective.

Exploring empowerment in settings: mapping distributions of network power.

Science.gov (United States)

Neal, Jennifer Watling

2014-06-01

This paper brings together two trends in the empowerment literature-understanding empowerment in settings and understanding empowerment as relational-by examining what makes settings empowering from a social network perspective. Specifically, extending Neal and Neal's (Am J Community Psychol 48(3/4):157-167, 2011) conception of network power, an empowering setting is defined as one in which (1) actors have existing relationships that allow for the exchange of resources and (2) the distribution of network power among actors in the setting is roughly equal. The paper includes a description of how researchers can examine distributions of network power in settings. Next, this process is illustrated in both an abstract example and using empirical data on early adolescents' peer relationships in urban classrooms. Finally, implications for theory, methods, and intervention related to understanding empowering settings are explored.

Modeling and optimization of an electric power distribution network ...

African Journals Online (AJOL)

Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

Recognition of power quality events by using multiwavelet-based neural networks

Energy Technology Data Exchange (ETDEWEB)

Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai [School of Electrical Engineering, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon Ratchasima 30000 (Thailand)

2008-05-15

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. (author)

Reliability assessment of power pole infrastructure incorporating deterioration and network maintenance

International Nuclear Information System (INIS)

Ryan, Paraic C.; Stewart, Mark G.; Spencer, Nathan; Li, Yue

2014-01-01

There is considerable investment in timber utility poles worldwide, and there is a need to examine the structural reliability and probability based management optimisation of these power distribution infrastructure elements. The work presented in this paper builds on the existing studies in this area through assessment of both treated and untreated timber power poles, with the effects of deterioration and network maintenance incorporated in the analysis. This more realistic assessment approach, with deterioration and maintenance considered, was achieved using event-based Monte Carlo simulation. The output from the probabilistic model is used to illustrate the importance of considering network maintenance in the time-dependent structural reliability assessment of timber power poles. Under wind load, treated and untreated poles designed and maintained in accordance with existing Australian standards were found to have similar failure rates. However, untreated pole networks required approximately twice as many maintenance based pole replacements to sustain the same level of reliability. The effect of four different network maintenance strategies on infrastructure performance was also investigated herein. This assessment highlighted the fact that slight alterations to network maintenance practices can lead to significant changes in performance of timber power pole networks. - Highlights: • A time-dependent structural reliability model was developed for timber power poles. • Deterioration and network maintenance were incorporated into this event based model. • Network maintenance had a significant impact on power pole wind vulnerability. • Treated and untreated poles designed to Australian standards had similar reliability. • Minor alterations to maintenance strategies had large effects on network performance

People identification for domestic non-overlapping RGB-D camera networks

NARCIS (Netherlands)

Takac, B.; Rauterberg, G.W.M.; Català, A.; Chen, W.

2015-01-01

The ability to identify the specific person in a home camera network is very relevant for healthcare applications where humans need to be observed daily in their living environment. The appearance based people identification in a domestic environment has many similarities with the problem of

Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Antônio Dâmaso

2017-11-01

Full Text Available Power consumption is a primary interest in Wireless Sensor Networks (WSNs, and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way.

Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks

Science.gov (United States)

Dâmaso, Antônio; Maciel, Paulo

2017-01-01

Power consumption is a primary interest in Wireless Sensor Networks (WSNs), and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way. PMID:29113078

Distributed control of deregulated electrical power networks

NARCIS (Netherlands)

Hermans, R.M.

2012-01-01

A prerequisite for reliable operation of electrical power networks is that supply and demand are balanced at all time, as efficient ways for storing large amounts of electrical energy are scarce. Balancing is challenging, however, due to the power system's dimensions and complexity, the low

Interference mitigation through adaptive power control in wireless sensor networks

NARCIS (Netherlands)

Chincoli, M.; Bacchiani, C.; Syed, Aly; Exarchakos, G.; Liotta, A.

2016-01-01

Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counter-productive

THE PROSPECTS OF DEVELOPMENT OF ELECTRIC POWER NETWORK IN GEORGIA

International Nuclear Information System (INIS)

Mshvidobadze, T.

2007-01-01

The possibility of application of one of the versions of development of the electric power network in Georgia is disscussed. The algorithm of grouping of the versions of power network development, which allows choosing the optimal network configuration under indefinite conditions, is offered. The experiments have demonstrated that the same optimal decision can be found by considerable reduction in the number of versions. (author)

Power Consumption Evaluation of Distributed Computing Network Considering Traffic Locality

Science.gov (United States)

Ogawa, Yukio; Hasegawa, Go; Murata, Masayuki

When computing resources are consolidated in a few huge data centers, a massive amount of data is transferred to each data center over a wide area network (WAN). This results in increased power consumption in the WAN. A distributed computing network (DCN), such as a content delivery network, can reduce the traffic from/to the data center, thereby decreasing the power consumed in the WAN. In this paper, we focus on the energy-saving aspect of the DCN and evaluate its effectiveness, especially considering traffic locality, i.e., the amount of traffic related to the geographical vicinity. We first formulate the problem of optimizing the DCN power consumption and describe the DCN in detail. Then, numerical evaluations show that, when there is strong traffic locality and the router has ideal energy proportionality, the system's power consumption is reduced to about 50% of the power consumed in the case where a DCN is not used; moreover, this advantage becomes even larger (up to about 30%) when the data center is located farthest from the center of the network topology.

Stability Analysis of Neural Networks-Based System Identification

Directory of Open Access Journals (Sweden)

Talel Korkobi

2008-01-01

Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

Power-Aware Routing and Network Design with Bundled Links: Solutions and Analysis

Directory of Open Access Journals (Sweden)

Rosario G. Garroppo

2013-01-01

Full Text Available The paper deeply analyzes a novel network-wide power management problem, called Power-Aware Routing and Network Design with Bundled Links (PARND-BL, which is able to take into account both the relationship between the power consumption and the traffic throughput of the nodes and to power off both the chassis and even the single Physical Interface Card (PIC composing each link. The solutions of the PARND-BL model have been analyzed by taking into account different aspects associated with the actual applicability in real network scenarios: (i the time for obtaining the solution, (ii the deployed network topology and the resulting topology provided by the solution, (iii the power behavior of the network elements, (iv the traffic load, (v the QoS requirement, and (vi the number of paths to route each traffic demand. Among the most interesting and novel results, our analysis shows that the strategy of minimizing the number of powered-on network elements through the traffic consolidation does not always produce power savings, and the solution of this kind of problems, in some cases, can lead to spliting a single traffic demand into a high number of paths.

Radioactivity nuclide identification based on BP and LM algorithm neural network

International Nuclear Information System (INIS)

Wang Jihong; Sun Jian; Wang Lianghou

2012-01-01

The paper provides the method which can identify radioactive nuclide based on the BP and LM algorithm neural network. Then, this paper compares the above-mentioned method with FR algorithm. Through the result of the Matlab simulation, the method of radioactivity nuclide identification based on the BP and LM algorithm neural network is superior to the FR algorithm. With the better effect and the higher accuracy, it will be the best choice. (authors)

On the identification of instabilities with neural networks on JET

International Nuclear Information System (INIS)

Murari, A.; Arena, P.; Buscarino, A.; Fortuna, L.; Iachello, M.

2013-01-01

JET plasmas are affected by various instabilities, which can be particularly dangerous in high performance discharges. An identification method, based on the use of advanced neural networks, called Recurrent Neural Networks (RNNs), has been applied to ELMs. The potential of the recurrent networks to identify the dynamics of the instabilities has been first tested using synthetic data. The networks have then been applied to JET experimental signals. An appropriate selection of the networks topology allows identifying quite well the time evolution of the edge temperature and of the magnetic fields, considered the best indicators of the ELMs. A quite limited number of periodic oscillations are used to train the networks, which then manage to follow quite well the dynamics of the instabilities, in a recurrent configuration on one of the inputs. The time evolution of the aforementioned signals, also during intervals not used in the training and never seen by the networks, are properly reproduced. A careful analysis of the various terms in the RNNs has the potential to give clear indications about the nature of these instabilities and their dynamical behaviour

High voltage power network construction

CERN Document Server

Harker, Keith

2018-01-01

This book examines the key requirements, considerations, complexities and constraints relevant to the task of high voltage power network construction, from design, finance, contracts and project management to installation and commissioning, with the aim of providing an overview of the holistic end to end construction task in a single volume.

Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks

Directory of Open Access Journals (Sweden)

M. Hadi Amini

2018-01-01

Full Text Available Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.

A Distributed Routing Scheme for Energy Management in Solar Powered Sensor Networks

KAUST Repository

Dehwah, Ahmad H.; Shamma, Jeff S.; Claudel, Christian G.

2017-01-01

Energy management is critical for solar-powered sensor networks. In this article, we consider data routing policies to optimize the energy in solar powered networks. Motivated by multipurpose sensor networks, the objective is to find the best

Effects of network node consolidation in optical access and aggregation networks on costs and power consumption

Science.gov (United States)

Lange, Christoph; Hülsermann, Ralf; Kosiankowski, Dirk; Geilhardt, Frank; Gladisch, Andreas

2010-01-01

The increasing demand for higher bit rates in access networks requires fiber deployment closer to the subscriber resulting in fiber-to-the-home (FTTH) access networks. Besides higher access bit rates optical access network infrastructure and related technologies enable the network operator to establish larger service areas resulting in a simplified network structure with a lower number of network nodes. By changing the network structure network operators want to benefit from a changed network cost structure by decreasing in short and mid term the upfront investments for network equipment due to concentration effects as well as by reducing the energy costs due to a higher energy efficiency of large network sites housing a high amount of network equipment. In long term also savings in operational expenditures (OpEx) due to the closing of central office (CO) sites are expected. In this paper different architectures for optical access networks basing on state-of-the-art technology are analyzed with respect to network installation costs and power consumption in the context of access node consolidation. Network planning and dimensioning results are calculated for a realistic network scenario of Germany. All node consolidation scenarios are compared against a gigabit capable passive optical network (GPON) based FTTH access network operated from the conventional CO sites. The results show that a moderate reduction of the number of access nodes may be beneficial since in that case the capital expenditures (CapEx) do not rise extraordinarily and savings in OpEx related to the access nodes are expected. The total power consumption does not change significantly with decreasing number of access nodes but clustering effects enable a more energyefficient network operation and optimized power purchase order quantities leading to benefits in energy costs.

Agent-based reactive power management of power distribution networks with distributed energy generation

International Nuclear Information System (INIS)

Rahman, M.S.; Mahmud, M.A.; Oo, A.M.T.; Pota, H.R.; Hossain, M.J.

2016-01-01

Highlights: • A coordinated multi-agent system is proposed for reactive power management. • A linear quadratic regulator with a proportional integral controller is designed. • Proposed multi-agent scheme provides accurate estimation and control of the system. • Voltage stability is improved with proper power management for different scenarios. • Results obtained from the proposed scheme is compared to the traditional approach. - Abstract: In this paper, a new agent-based distributed reactive power management scheme is proposed to improve the voltage stability of energy distribution systems with distributed generation units. Three types of agents – distribution system agent, estimator agent, and control agent are developed within the multi-agent framework. The agents simultaneously coordinated their activities through the online information and energy flow. The overall achievement of the proposed scheme depends on the coordination between two tasks – (i) estimation of reactive power using voltage variation formula and (ii) necessary control actions to provide the estimated reactive power to the distribution networks through the distributed static synchronous compensators. A linear quadratic regulator with a proportional integrator is designed for the control agent in order to control the reactive component of the current and the DC voltage of the compensators. The performance of the proposed scheme is tested on a 10-bus power distribution network under various scenarios. The effectiveness is validated by comparing the proposed approach to the conventional proportional integral control approach. It is found that, the agent-based scheme provides excellent robust performance under various operating conditions of the power distribution network.

Understanding the tariff. Access to the public power transportation network

International Nuclear Information System (INIS)

2002-01-01

Since the European directive of December 19, 1996 about the common rules of the European power market, the eligible companies can chose their power supplier anywhere in Europe. The manager of the French power transportation network (RTE) supplies a network access to these companies according to a tariff fixed by the decree no. 2002-1014 from July 19, 2002. The aim of this document is to explain this tariff: tariffing principles ('mail-stamp' principle, voltage domain, subscribed output power tariffs, input power tariffs), tariffing elements (access to the grid, elements of output tariffs (subscribed power, overload, emergency tariffs, modifications etc..)), invoicing modalities, output tariffs, definitions. (J.S.)

Advanced Power Converter for Universal and Flexible Power Management in Future Electricity Network

DEFF Research Database (Denmark)

Iov, Florin; Blaabjerg, Frede; Bassett, R.

2007-01-01

converters for grid connection of renewable sources will be needed. These power converters must be able to provide intelligent power management as well as ancillary services. This paper presents the overall structure and the control aspects of an advanced power converter for universal and flexible power......More "green" power provided by Distributed Generation will enter into the European electricity network in the near future. In order to control the power flow and to ensure proper and secure operation of this future grid, with an increased level of the renewable power, new power electronic...

Braess's paradox in oscillator networks, desynchronization and power outage

International Nuclear Information System (INIS)

Witthaut, Dirk; Timme, Marc

2012-01-01

Robust synchronization is essential to ensure the stable operation of many complex networked systems such as electric power grids. Increasing energy demands and more strongly distributing power sources raise the question of where to add new connection lines to the already existing grid. Here we study how the addition of individual links impacts the emergence of synchrony in oscillator networks that model power grids on coarse scales. We reveal that adding new links may not only promote but also destroy synchrony and link this counter-intuitive phenomenon to Braess's paradox known for traffic networks. We analytically uncover its underlying mechanism in an elementary grid example, trace its origin to geometric frustration in phase oscillators, and show that it generically occurs across a wide range of systems. As an important consequence, upgrading the grid requires particular care when adding new connections because some may destabilize the synchronization of the grid—and thus induce power outages. (paper)

Development scheme of the public power transportation network

International Nuclear Information System (INIS)

2005-01-01

Article 14 of the modified law from February 10, 2000 relative to the modernization and development of the electric utility foresees that the development scheme of the public power transportation network is regularly submitted to the approval of the ministry of energy after advice from the energy regulation commission. The development scheme identifies the areas of 'power fragility' with respect to the existing or future constraints susceptible to occur at the short- or medium-term on the French power grid. This document comprises the text of the law 2000-108 from February 10, 2000, and the complete development scheme with its appendixes (regulatory and administrative context relative to network projects, constraints relative to each administrative region). (J.S.)

System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

CERN Document Server

Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

2014-01-01

Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

Power Laws, Scale-Free Networks and Genome Biology

CERN Document Server

Koonin, Eugene V; Karev, Georgy P

2006-01-01

Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

Output power distributions of terminals in a 3G mobile communication network.

Science.gov (United States)

Persson, Tomas; Törnevik, Christer; Larsson, Lars-Eric; Lovén, Jan

2012-05-01

The objective of this study was to examine the distribution of the output power of mobile phones and other terminals connected to a 3G network in Sweden. It is well known that 3G terminals can operate with very low output power, particularly for voice calls. Measurements of terminal output power were conducted in the Swedish TeliaSonera 3G network in November 2008 by recording network statistics. In the analysis, discrimination was made between rural, suburban, urban, and dedicated indoor networks. In addition, information about terminal output power was possible to collect separately for voice and data traffic. Information from six different Radio Network Controllers (RNCs) was collected during at least 1 week. In total, more than 800000 h of voice calls were collected and in addition to that a substantial amount of data traffic. The average terminal output power for 3G voice calls was below 1 mW for any environment including rural, urban, and dedicated indoor networks. This is <1% of the maximum available output power. For data applications the average output power was about 6-8 dB higher than for voice calls. For rural areas the output power was about 2 dB higher, on average, than in urban areas. Copyright © 2011 Wiley Periodicals, Inc.

Communication Network Architectures Based on Ethernet Passive Optical Network for Offshore Wind Power Farms

Directory of Open Access Journals (Sweden)

Mohamed A. Ahmed

2016-03-01

Full Text Available Nowadays, with large-scale offshore wind power farms (WPFs becoming a reality, more efforts are needed to maintain a reliable communication network for WPF monitoring. Deployment topologies, redundancy, and network availability are the main items to enhance the communication reliability between wind turbines (WTs and control centers. Traditional communication networks for monitoring and control (i.e., supervisory control and data acquisition (SCADA systems using switched gigabit Ethernet will not be sufficient for the huge amount of data passing through the network. In this paper, the optical power budget, optical path loss, reliability, and network cost of the proposed Ethernet Passive Optical Network (EPON-based communication network for small-size offshore WPFs have been evaluated for five different network architectures. The proposed network model consists of an optical network unit device (ONU deployed on the WT side for collecting data from different internal networks. All ONUs from different WTs are connected to a central optical line terminal (OLT, placed in the control center. There are no active electronic elements used between the ONUs and the OLT, which reduces the costs and complexity of maintenance and deployment. As fiber access networks without any protection are characterized by poor reliability, three different protection schemes have been configured, explained, and discussed. Considering the cost of network components, the total implementation expense of different architectures with, or without, protection have been calculated and compared. The proposed network model can significantly contribute to the communication network architecture for next generation WPFs.

An Explication and Test of Communication Network Content and Multiplexity as Predictors of Organizational Identification.

Science.gov (United States)

Bullis, Connie; Bach, Betsy Wackernagel

1991-01-01

Examines the relationship between identification and communication using organizational identification (OI) as a theoretical framework for studying communication networks among incoming graduate students in three university departments of communication. Concludes that, irrespective of initial OI, stronger initial multiplexity predicts the growth…

Substructure identification for shear structures: cross-power spectral density method

International Nuclear Information System (INIS)

Zhang, Dongyu; Johnson, Erik A

2012-01-01

In this paper, a substructure identification method for shear structures is proposed. A shear structure is divided into many small substructures; utilizing the dynamic equilibrium of a one-floor substructure, an inductive identification problem is formulated, using the cross-power spectral densities between structural floor accelerations and a reference response, to estimate the parameters of that one story. Repeating this procedure, all story parameters of the shear structure are identified from top to bottom recursively. An identification error analysis is performed for the proposed substructure method, revealing how uncertain factors (e.g. measurement noise) in the identification process affect the identification accuracy. According to the error analysis, a smart reference selection rule is designed to choose the optimal reference response that further enhances the identification accuracy. Moreover, based on the identification error analysis, explicit formulae are developed to calculate the variances of the parameter identification errors. A ten-story shear structure is used to illustrate the effectiveness of the proposed substructure method. The simulation results show that the method, combined with the reference selection rule, can very accurately identify structural parameters despite large measurement noise. Furthermore, the proposed formulae provide good predictions for the variances of the parameter identification errors, which are vital for providing accurate warnings of structural damage. (paper)

Role of centrality for the identification of influential spreaders in complex networks.

Science.gov (United States)

de Arruda, Guilherme Ferraz; Barbieri, André Luiz; Rodríguez, Pablo Martín; Rodrigues, Francisco A; Moreno, Yamir; Costa, Luciano da Fontoura

2014-09-01

The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.

Relay Protection Coordination for Photovoltaic Power Plant Connected on Distribution Network

OpenAIRE

Nikolovski, Srete; Papuga, Vanja; Knežević, Goran

2014-01-01

This paper presents a procedure and computation of relay protection coordination for a PV power plant connected to the distribution network. In recent years, the growing concern for environment preservation has caused expansion of photovoltaic PV power plants in distribution networks. Numerical computer simulation is an indispensable tool for studying photovoltaic (PV) systems protection coordination. In this paper, EasyPower computer program is used with the module Power Protector. Time-curr...

Economic benefits of broadened local area networks for electric power plants

International Nuclear Information System (INIS)

Holmes, T.

1988-01-01

The paper discusses economic benefits which influenced the choice of a broadband local area network for a power plant instead of an alternative multi-cable communication network. Broadband communication networks can offer significant economies over alternative technologies. One-time, cost avoidance savings and recurring annual savings are estimated to total $5.1 million in the first year. The cost/benefit analysis presented here can be used as a guide by other utilities to analyze communication networking alternatives. The paper also includes a discussion of local area network attributes relevant to the power plant installation

The application of neural networks to flow regime identification

International Nuclear Information System (INIS)

Embrechts, M.; Yapo, T.C.; Lahey, R.T. Jr.

1993-01-01

This paper deals with the application of a Kohonen map for the identification of two-phase flow regimes where a mixture of gas and fluid flows through a horizontal tube. Depending on the relative flow velocities of the gas and the liquid phase, four distinct flow regimes can be identified: Wavy flow, plug flow, slug flow and annular flow. A schematic of these flow regimes is presented. The objective identification of two-phase flow regimes constitutes an important and challenging problem for the design of safe and reliable nuclear power plants. Previous attempts to classify these flow regimes are reviewed by Franca and Lahey. The authors describe how a Kohonen map can be applied to distinguish between flow regimes based on the Fourier power spectra and wavelet transforms of pressure drop fluctuations. The Fourier power spectra allowed the Kohonen map to identify the flow regimes successfully. In contrast, the Kohonen maps based on a wavelet transform could only distinguish between wavy and annular flows. An analysis of typical two-phase pressure drop data for an air/water mixture in a horizontal pipe is presented. Use of the wavelet transform and the Kohonen feature map are discussed

Power Allocation and Outage Probability Analysis for SDN-based Radio Access Networks

Science.gov (United States)

Zhao, Yongxu; Chen, Yueyun; Mai, Zhiyuan

2018-01-01

In this paper, performance of Access network Architecture based SDN (Software Defined Network) is analyzed with respect to the power allocation issue. A power allocation scheme PSO-PA (Particle Swarm Optimization-power allocation) algorithm is proposed, the proposed scheme is subjected to constant total power with the objective of minimizing system outage probability. The entire access network resource configuration is controlled by the SDN controller, then it sends the optimized power distribution factor to the base station source node (SN) and the relay node (RN). Simulation results show that the proposed scheme reduces the system outage probability at a low complexity.

On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

DEFF Research Database (Denmark)

Alizadeh, Tohid

2008-01-01

This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

Dynamically coated capillaries improve the identification power of capillary zone electrophoresis for basic drugs in toxicological analysis

NARCIS (Netherlands)

Boone, C.M; Jonkers, E.Z; Franke, J.P.; de Zeeuw, R.A; Ensing, K

2001-01-01

In systematic toxicological analysis (STA), analytical methods should have a high identification power. This can be suitably expressed by parameters such as mean list length (MLL) or discriminating power (DP). The reproducibility of a method has a great impact on its identification power, and should

Efficient Reactive Power Compensation Algorithm for Distribution Network

Directory of Open Access Journals (Sweden)

J. Jerome

2017-12-01

Full Text Available The use of automation and energy efficient equipment with electronic control would greatly improve industrial production.  These new devices are more sensitive to supply voltage deviation and the characteristics of the power system that was previously ignored are now very important. Hence the benefits of distribution automation have been widely acknowledged in recent years. This paper proposes an efficient load flow solution technique extended to find optimum location for reactive power compensation and network reconfiguration for planning and day-to-day operation of distribution networks.  This is required as a part of the distribution automation system (DAS for taking various control and operation decisions.  The method exploits the radial nature of the network and uses forward and backward propagation technique to calculate branch currents and node voltages.  The proposed method has been tested to analyze several practical distribution networks of various voltage levels and also having high R/X ratio.

Power-Hop: A Pervasive Observation for Real Complex Networks

Science.gov (United States)

2016-03-14

e.g., power grid, the Internet and the web-graph), social (e.g., friendship networks — Facebook , Gowalla—and co- authorship networks ), urban (e.g...Mislove A., Cha M. and Gummadi K.P. On the evolution of user interaction in Facebook . In Proc. Workshop on Online Social Networks 2009. doi...scale-free distribution is pervasive and describes a large variety of networks , ranging from social and urban to technological and biological networks

Identification of hadronic tau decays at the ATLAS detector using artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Duschinger, Dirk; Hanisch, Stefanie; Mader, Wolfgang; Madysa, Nico; Straessner, Arno [Institut fuer Kern- und Teilchenphysik, TU Dresden (Germany)

2016-07-01

One of the primary goals of the ATLAS experiment at the LHC is the search for physics beyond the Standard Model. The efficient identification of hadronically decaying tau leptons is crucial for this as they comprise the final states of several decay channels sensitive to new physics. (e. g. Higgs boson decays H → τ{sub had} τ{sub had}) The identification algorithm currently applied at ATLAS utilizes multi-variate methods and reconstructed particle properties to discriminate against QCD jets, which constitute an important background. This talk presents a new neural-network-based approach to hadronic tau decay identification and investigates its dependence on hyperparameters such as the network topology or number of training cycles. Ensembling is presented as a technique to improve classifier performance and robustness against overtraining. The resulting classifier is compared to the current approach based on Boosted Decision Trees. The study is based on 2012 data taken at the ATLAS detector at a center-of-mass energy of √(s)=8 TeV.

On joint power allocation and multipath routing in femto-relay networks

OpenAIRE

Hoteit , Sahar; Duhamel , Pierre; Lasaulce , Samson

2016-01-01

International audience; —Transmit power allocation techniques are very important to manage interference in small-cell networks. While available power allocation algorithms in the literature rely on a predefined routing protocol, we propose in this paper a power-efficient two-step algorithm that allows power allocation and routing to be performed jointly in femto-relay networks. First, we propose an interference-based partitioning method to cluster the femto-relays, then we adopt an iterative ...

Network Understanding of Herb Medicine via Rapid Identification of Ingredient-Target Interactions

Science.gov (United States)

Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

2014-01-01

Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.

Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

CERN Document Server

Patan, Maciej

2012-01-01

Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

Strategies for Power/Energy Saving in Distribution Networks

Directory of Open Access Journals (Sweden)

GRIGORAS, G.

2010-05-01

Full Text Available The power/energy losses reduction in distribution systems is an important issue during planning and operation, with important technical and economical implications. Thus, the energy losses minimization implies not only the technical improvement of the network, through its renewal with the introduction of the technological innovations in the equipment and circuit components as well as the optimal planning of the design and development of the network, but also requires the use of the methods and software tools to facilitate the operation process. The paper presents a strategy for power/energy saving which replacement of the 6 kV voltage level with 20 kV voltage level in correlation with the extent of using efficient transformers. In this line, different urban distribution networks were analyzed using fuzzy techniques.

Open-WiSe: A Solar Powered Wireless Sensor Network Platform

Science.gov (United States)

González, Apolinar; Aquino, Raúl; Mata, Walter; Ochoa, Alberto; Saldaña, Pedro; Edwards, Arthur

2012-01-01

Because battery-powered nodes are required in wireless sensor networks and energy consumption represents an important design consideration, alternate energy sources are needed to provide more effective and optimal function. The main goal of this work is to present an energy harvesting wireless sensor network platform, the Open Wireless Sensor node (WiSe). The design and implementation of the solar powered wireless platform is described including the hardware architecture, firmware, and a POSIX Real-Time Kernel. A sleep and wake up strategy was implemented to prolong the lifetime of the wireless sensor network. This platform was developed as a tool for researchers investigating Wireless sensor network or system integrators. PMID:22969396

Open-WiSe: a solar powered wireless sensor network platform.

Science.gov (United States)

González, Apolinar; Aquino, Raúl; Mata, Walter; Ochoa, Alberto; Saldaña, Pedro; Edwards, Arthur

2012-01-01

Because battery-powered nodes are required in wireless sensor networks and energy consumption represents an important design consideration, alternate energy sources are needed to provide more effective and optimal function. The main goal of this work is to present an energy harvesting wireless sensor network platform, the Open Wireless Sensor node (WiSe). The design and implementation of the solar powered wireless platform is described including the hardware architecture, firmware, and a POSIX Real-Time Kernel. A sleep and wake up strategy was implemented to prolong the lifetime of the wireless sensor network. This platform was developed as a tool for researchers investigating Wireless sensor network or system integrators.

Wireless Infrastructure M2M Network For Distributed Power Grid Monitoring.

Science.gov (United States)

Gharavi, Hamid; Hu, Bin

2017-01-01

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network.

Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

International Nuclear Information System (INIS)

Bobin, C.; Bichler, O.; Lourenço, V.; Thiam, C.; Thévenin, M.

2016-01-01

Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes’ rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ( 241 Am, 133 Ba, 207 Bi, 60 Co, 137 Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. - Highlights: • A fast radionuclide identification algorithm applicable in spectroscopic portal monitors is presented. • The proposed algorithm combines a Bayesian sequential approach and a spiking neural network. • The algorithm was validated using the mixture of γ-emitter spectra provided by a well-type NaI(Tl) detector. • The radionuclide identification process is implemented using the whole γ-spectrum without energy calibration.

Improvement of and Parameter Identification for the Bimodal Time-Varying Modified Kanai-Tajimi Power Spectral Model

Directory of Open Access Journals (Sweden)

Huiguo Chen

2017-01-01

Full Text Available Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures.

Output power distributions of mobile radio base stations based on network measurements

International Nuclear Information System (INIS)

Colombi, D; Thors, B; Persson, T; Törnevik, C; Wirén, N; Larsson, L-E

2013-01-01

In this work output power distributions of mobile radio base stations have been analyzed for 2G and 3G telecommunication systems. The approach is based on measurements in selected networks using performance surveillance tools part of the network Operational Support System (OSS). For the 3G network considered, direct measurements of output power levels were possible, while for the 2G networks, output power levels were estimated from measurements of traffic volumes. Both voice and data services were included in the investigation. Measurements were conducted for large geographical areas, to ensure good overall statistics, as well as for smaller areas to investigate the impact of different environments. For high traffic hours, the 90th percentile of the averaged output power was found to be below 65% and 45% of the available output power for the 2G and 3G systems, respectively.

Output power distributions of mobile radio base stations based on network measurements

Science.gov (United States)

Colombi, D.; Thors, B.; Persson, T.; Wirén, N.; Larsson, L.-E.; Törnevik, C.

2013-04-01

In this work output power distributions of mobile radio base stations have been analyzed for 2G and 3G telecommunication systems. The approach is based on measurements in selected networks using performance surveillance tools part of the network Operational Support System (OSS). For the 3G network considered, direct measurements of output power levels were possible, while for the 2G networks, output power levels were estimated from measurements of traffic volumes. Both voice and data services were included in the investigation. Measurements were conducted for large geographical areas, to ensure good overall statistics, as well as for smaller areas to investigate the impact of different environments. For high traffic hours, the 90th percentile of the averaged output power was found to be below 65% and 45% of the available output power for the 2G and 3G systems, respectively.

A novel power efficient location-based cooperative routing with transmission power-upper-limit for wireless sensor networks.

Science.gov (United States)

Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

2013-05-15

The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate.

A Novel Power Efficient Location-Based Cooperative Routing with Transmission Power-Upper-Limit for Wireless Sensor Networks

Science.gov (United States)

Shi, Juanfei; Calveras, Anna; Cheng, Ye; Liu, Kai

2013-01-01

The extensive usage of wireless sensor networks (WSNs) has led to the development of many power- and energy-efficient routing protocols. Cooperative routing in WSNs can improve performance in these types of networks. In this paper we discuss the existing proposals and we propose a routing algorithm for wireless sensor networks called Power Efficient Location-based Cooperative Routing with Transmission Power-upper-limit (PELCR-TP). The algorithm is based on the principle of minimum link power and aims to take advantage of nodes cooperation to make the link work well in WSNs with a low transmission power. In the proposed scheme, with a determined transmission power upper limit, nodes find the most appropriate next nodes and single-relay nodes with the proposed algorithm. Moreover, this proposal subtly avoids non-working nodes, because we add a Bad nodes Avoidance Strategy (BAS). Simulation results show that the proposed algorithm with BAS can significantly improve the performance in reducing the overall link power, enhancing the transmission success rate and decreasing the retransmission rate. PMID:23676625

Identification and management of distributed data NGN, content-centric networks and the web

CERN Document Server

Bartolomeo, Giovanni

2013-01-01

Although several books and academic courses discuss data management and networking, few of them focus on the convergence of networking and software technologies for identifying, addressing, and managing distributed data. Focusing on this convergence, Identification and Management of Distributed Data: NGN, Content-Centric Networks and the Web collates and describes the various distributed data management technologies to help readers from various backgrounds understand the common aspects that govern distributed data management. With a focus on the primary problems in identifying, addressing, and

Low-Power Wireless Sensor Networks Protocols, Services and Applications

CERN Document Server

Suhonen, Jukka; Kaseva, Ville; Hämäläinen, Timo D; Hännikäinen, Marko

2012-01-01

Wireless sensor network (WSN) is an ad-hoc network technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless networking. The applications for sensor networks range from home and industrial environments to military uses. Unlike the traditional computer networks, a WSN is application-oriented and deployed for a specific task. WSNs are data centric, which means that messages are not send to individual nodes but to geographical locations or regions based on the data content. A WSN node is typically battery powered and characterized by extremely small size and low cost. As a result, the processing power, memory, and energy resources of an individual sensor node are limited. However, the feasibility of a WSN lies on the collaboration between the nodes. A reference WSN node comprises a Micro-Controller Unit (MCU) having few Million Instructions Per Second (MIPS) processing speed, tens of kilobytes program memory, few kilobytes data m...

Joint Power Allocation and Beamforming in Amplify-and-Forward Relay Networks under Per-Node Power Constraint

Directory of Open Access Journals (Sweden)

Farzin Azami

2017-01-01

Full Text Available Two-way relay networks (TWRN have been intensively investigated over the past decade due to their ability to enhance the performance assessment of networks in terms of cellular coverage and spectral efficiency. Yet, power control in such systems is a nontrivial issue, particularly in multirelay networks where relays are deployed to ensure a required Quality of Service (QoS. In this paper, we envision to address this critical issue by minimizing the sum-power with respect to per-node power consumption and acceptable users’ rates. To tackle this, we employ a variable transformation to turn the fractional quadratically constrained quadratic problem (QCQP into semidefinite programming (SDP. This algorithm is also extended to a distributed format. Simulation results of deploying 10 relay stations reveal that the total power consumption will decrease to approximately 8 dBW for 6 bps/Hz sum-rate.

A Maximum Power Transfer Tracking Method for WPT Systems with Coupling Coefficient Identification Considering Two-Value Problem

Directory of Open Access Journals (Sweden)

Xin Dai

2017-10-01

Full Text Available Maximum power transfer tracking (MPTT is meant to track the maximum power point during the system operation of wireless power transfer (WPT systems. Traditionally, MPTT is achieved by impedance matching at the secondary side when the load resistance is varied. However, due to a loosely coupling characteristic, the variation of coupling coefficient will certainly affect the performance of impedance matching, therefore MPTT will fail accordingly. This paper presents an identification method of coupling coefficient for MPTT in WPT systems. Especially, the two-value issue during the identification is considered. The identification approach is easy to implement because it does not require additional circuit. Furthermore, MPTT is easy to realize because only two easily measured DC parameters are needed. The detailed identification procedure corresponding to the two-value issue and the maximum power transfer tracking process are presented, and both the simulation analysis and experimental results verified the identification method and MPTT.

Artificial Neural Network Application for Power Transfer Capability and Voltage Calculations in Multi-Area Power System

Directory of Open Access Journals (Sweden)

Palukuru NAGENDRA

2010-12-01

Full Text Available In this study, the use of artificial neural network (ANN based model, multi-layer perceptron (MLP network, to compute the transfer capabilities in a multi-area power system was explored. The input for the ANN is load status and the outputs are the transfer capability among the system areas, voltage magnitudes and voltage angles at concerned buses of the areas under consideration. The repeated power flow (RPF method is used in this paper for calculating the power transfer capability, voltage magnitudes and voltage angles necessary for the generation of input-output patterns for training the proposed MLP neural network. Preliminary investigations on a three area 30-bus system reveal that the proposed model is computationally faster than the conventional method.

AS Migration and Optimization of the Power Integrated Data Network

Science.gov (United States)

Zhou, Junjie; Ke, Yue

2018-03-01

In the transformation process of data integration network, the impact on the business has always been the most important reference factor to measure the quality of network transformation. With the importance of the data network carrying business, we must put forward specific design proposals during the transformation, and conduct a large number of demonstration and practice to ensure that the transformation program meets the requirements of the enterprise data network. This paper mainly demonstrates the scheme of over-migrating point-to-point access equipment in the reconstruction project of power data comprehensive network to migrate the BGP autonomous domain to the specified domain defined in the industrial standard, and to smooth the intranet OSPF protocol Migration into ISIS agreement. Through the optimization design, eventually making electric power data network performance was improved on traffic forwarding, traffic forwarding path optimized, extensibility, get larger, lower risk of potential loop, the network stability was improved, and operational cost savings, etc.

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

Science.gov (United States)

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Application of artificial neural networks to improve power transfer ...

African Journals Online (AJOL)

Application of artificial neural networks to improve power transfer capability through OLTC. ... International Journal of Engineering, Science and Technology ... Numerical results show that the setting of OLTC transformer in terms of the load model has a major effect on the maximum power transfer in power systems and the ...

Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

International Nuclear Information System (INIS)

Hiroshi Goda; Seungjin Kim; Ye Mi; Finch, Joshua P.; Mamoru Ishii; Jennifer Uhle

2002-01-01

Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated. (authors)

RBF Neural Network Approach for Identification and Control of DC Motors

Directory of Open Access Journals (Sweden)

EA Feilat

2012-12-01

Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Mining Heterogeneous Information Networks by Exploring the Power of Links

Science.gov (United States)

Han, Jiawei

Knowledge is power but for interrelated data, knowledge is often hidden in massive links in heterogeneous information networks. We explore the power of links at mining heterogeneous information networks with several interesting tasks, including link-based object distinction, veracity analysis, multidimensional online analytical processing of heterogeneous information networks, and rank-based clustering. Some recent results of our research that explore the crucial information hidden in links will be introduced, including (1) Distinct for object distinction analysis, (2) TruthFinder for veracity analysis, (3) Infonet-OLAP for online analytical processing of information networks, and (4) RankClus for integrated ranking-based clustering. We also discuss some of our on-going studies in this direction.

Open-WiSe: A Solar Powered Wireless Sensor Network Platform

Directory of Open Access Journals (Sweden)

Arthur Edwards

2012-06-01

Full Text Available Because battery-powered nodes are required in wireless sensor networks and energy consumption represents an important design consideration, alternate energy sources are needed to provide more effective and optimal function. The main goal of this work is to present an energy harvesting wireless sensor network platform, the Open Wireless Sensor node (WiSe. The design and implementation of the solar powered wireless platform is described including the hardware architecture, firmware, and a POSIX Real-Time Kernel. A sleep and wake up strategy was implemented to prolong the lifetime of the wireless sensor network. This platform was developed as a tool for researchers investigating Wireless sensor network or system integrators.

Identification and simulation of the power quality problems using computer models

International Nuclear Information System (INIS)

Abro, M.R.; Memon, A.P.; Memon, Z.A.

2005-01-01

The Power Quality has become the main factor in our life. If this quality of power is being polluted over the Electrical Power Network, serious problems could arise within the modem social structure and its conveniences. The Nonlinear Characteristics of various office and Industrial equipment connected to the power grid could cause electrical disturbances to poor power quality. In many cases the electric power consumed is first converted to different form and such conversion process introduces harmonic pollution in the grid. These electrical disturbances could destroy certain sensitive equipment connected to the grid or in some cases could cause them to malfunction. In the huge power network identifying the source of such disturbance without causing interruption to the supply is a big problem. This paper attempts to study the power quality problem caused by typical loads using computer models paving the way to identify the source of the problem. PSB (Power System Blockset) Toolbox of MATLAB is used for this paper, which is designed to provide modem tool that rapidly and easily builds models and simulates the power system. The blockset uses the Simulink environment, allowing a model to be built using simple click and drag procedures. (author)

Downlink power distributions for 2G and 3G mobile communication networks.

Science.gov (United States)

Colombi, Davide; Thors, Björn; Persson, Tomas; Wirén, Niklas; Larsson, Lars-Eric; Jonsson, Mikael; Törnevik, Christer

2013-12-01

Knowledge of realistic power levels is key when conducting accurate EMF exposure assessments. In this study, downlink output power distributions for radio base stations in 2G and 3G mobile communication networks have been assessed. The distributions were obtained from network measurement data collected from the Operations Support System, which normally is used for network monitoring and management. Significant amounts of data were gathered simultaneously for large sets of radio base stations covering wide geographical areas and different environments. The method was validated with in situ measurements. For the 3G network, the 90th percentile of the averaged output power during high traffic hours was found to be 43 % of the maximum available power. The corresponding number for 2G, with two or more transceivers installed, was 65 % or below.

Effect of size heterogeneity on community identification in complex networks

Energy Technology Data Exchange (ETDEWEB)

Danon, L.; Diaz-Guilera, A.; Arenas, A.

2008-01-01

Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

Linear Programming Approaches for Power Savings in Software-defined Networks

NARCIS (Netherlands)

Moghaddam, F.A.; Grosso, P.

2016-01-01

Software-defined networks have been proposed as a viable solution to decrease the power consumption of the networking component in data center networks. Still the question remains on which scheduling algorithms are most suited to achieve this goal. We propose 4 different linear programming

Highly identified power-holders feel responsible: The interplay between social identification and social power within groups.

Science.gov (United States)

Scholl, Annika; Sassenberg, Kai; Ellemers, Naomi; Scheepers, Daan; de Wit, Frank

2018-01-01

Power relations affect dynamics within groups. Power-holders' decisions not only determine their personal outcomes, but also the outcomes of others in the group that they control. Yet, power-holders often tend to overlook this responsibility to take care of collective interests. The present research investigated how social identification - with the group to which both the powerful and the powerless belong - alters perceived responsibility among power-holders (and the powerless). Combining research on social power and social identity, we argue that power-holders perceive more responsibility than the powerless when strongly (rather than when weakly) identifying with the group. A study among leaders and an experiment supported this, highlighting that although power-holders are often primarily concerned about personal outcomes, they do feel responsible for considering others' interests when these others are included in the (social) self. © 2017 The British Psychological Society.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

Science.gov (United States)

Peng, Jinzhu; Dubay, Rickey

2011-10-01

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Wirelessly powered sensor networks and computational RFID

CERN Document Server

2013-01-01

The Wireless Identification and Sensing Platform (WISP) is the first of a new class of RF-powered sensing and computing systems.  Rather than being powered by batteries, these sensor systems are powered by radio waves that are either deliberately broadcast or ambient.  Enabled by ongoing exponential improvements in the energy efficiency of microelectronics, RF-powered sensing and computing is rapidly moving along a trajectory from impossible (in the recent past), to feasible (today), toward practical and commonplace (in the near future). This book is a collection of key papers on RF-powered sensing and computing systems including the WISP.  Several of the papers grew out of the WISP Challenge, a program in which Intel Corporation donated WISPs to academic applicants who proposed compelling WISP-based projects.  The book also includes papers presented at the first WISP Summit, a workshop held in Berkeley, CA in association with the ACM Sensys conference, as well as other relevant papers. The book provides ...

Response of pressurized water reactor (PWR) to network power generation demands

International Nuclear Information System (INIS)

Schreiner, L.A.

1991-01-01

The flexibility of the PWR type reactor in terms of response to the variations of the network power demands, is demonstrated. The factors that affect the transitory flexibility and some design prospects that allow the reactor fits the requirements of the network power demands, are also discussed. (M.J.A.)

Electric power plants and networks. Elektrische Kraftwerke

Energy Technology Data Exchange (ETDEWEB)

Happoldt, H [Brown, Boveri und Cie A.G., Mannheim (Germany, F.R.). Abt. Centralen; Oeding, D [Brown, Boveri und Cie A.G., Mannheim (Germany, F.R.). Zentralbereich Forschung und Entwicklung

1978-01-01

This book is itended for enginers working in the planning, construction and operation of plants to generate and distribute electric power; it is also a valuable aid for students of power engineering. This new edition places more emphasis on the presentation and calculation of three-phase current networks with the aid of symmetric components. The equations used for calculation are adapted to VDE regulations as far as possible.

Reliability Analysis Techniques for Communication Networks in Nuclear Power Plant

International Nuclear Information System (INIS)

Lim, T. J.; Jang, S. C.; Kang, H. G.; Kim, M. C.; Eom, H. S.; Lee, H. J.

2006-09-01

The objectives of this project is to investigate and study existing reliability analysis techniques for communication networks in order to develop reliability analysis models for nuclear power plant's safety-critical networks. It is necessary to make a comprehensive survey of current methodologies for communication network reliability. Major outputs of this study are design characteristics of safety-critical communication networks, efficient algorithms for quantifying reliability of communication networks, and preliminary models for assessing reliability of safety-critical communication networks

Generalised power graph compression reveals dominant relationship patterns in complex networks.

Science.gov (United States)

Ahnert, Sebastian E

2014-03-25

We introduce a framework for the discovery of dominant relationship patterns in complex networks, by compressing the networks into power graphs with overlapping power nodes. When paired with enrichment analysis of node classification terms, the most compressible sets of edges provide a highly informative sketch of the dominant relationship patterns that define the network. In addition, this procedure also gives rise to a novel, link-based definition of overlapping node communities in which nodes are defined by their relationships with sets of other nodes, rather than through connections within the community. We show that this completely general approach can be applied to undirected, directed, and bipartite networks, yielding valuable insights into the large-scale structure of real-world networks, including social networks and food webs. Our approach therefore provides a novel way in which network architecture can be studied, defined and classified.

RF Wireless Power Transfer: Regreening Future Networks

OpenAIRE

Tran, Ha-Vu; Kaddoum, Georges

2017-01-01

Green radio communication is an emerging topic since the overall footprint of information and communication technology (ICT) services is predicted to triple between 2007 and 2020. Given this research line, energy harvesting (EH) and wireless power transfer (WPT) networks can be evaluated as promising approaches. In this paper, an overview of recent trends for future green networks on the platforms of EH and WPT is provided. By rethinking the application of radio frequency (RF)-WPT, a new conc...

Characterizations of the Beta and the Degree Network Power Measure

NARCIS (Netherlands)

van den Brink, J.R.; Borm, P.; Hendrickx, R.; Owen, G.

2008-01-01

A symmetric network consists of a set of positions and a set of bilateral links between these positions. For every symmetric network we define a cooperative transferable utility game that measures the "power" of each coalition of positions in the network. Applying the Shapley value to this game

Joint Design of Control and Power Efficiency in Wireless Networked Control System

Directory of Open Access Journals (Sweden)

Yan Wang

2014-01-01

Full Text Available This paper presents a joint design method for wireless networked control system (WNCS to balance both the demands of network service and the control performance. Since the problems of power consumption, communication reliability, and system stability exist simultaneously and interdependently in WNCS, most of the achieved results in the wireless network and wired networked control system cannot be used directly. To coordinate the three problems, sampling period is found to be the linking bridge. An adaptive sampling power efficiency algorithm is proposed to manage the power consumption such that it can meet the demands of network life span. The sampling period is designed to update periodically on the constraints of network schedulability and system stability. The convergence of the power efficiency algorithm is further proved. The sampling period is no longer a fixed value, however; thus, increasing the difficulty in modeling and controlling such a complicated time-varying system remains. In this work, a switched control system scheme is applied to model such a WNCS, and the effect of network-induced delay is considered. Switched feedback controllers are introduced to stabilize the WNCS, and some considerations on stability condition and the bounds of the update circle for renewing sampling period are discussed. A numerical example shows the effectiveness of the proposed method.

Distributed routing algorithms to manage power flow in agent-based active distribution network

NARCIS (Netherlands)

Nguyen, H.P.; Kling, W.L.; Georgiadis, G.; Papatriantafilou, M.; Anh-Tuan, L.; Bertling, L.

2010-01-01

The current transition from passive to active electric distribution networks comes with problems and challenges on bi-directional power flow in the network and the uncertainty in the forecast of power generation from grid-connected renewable and distributed energy sources. The power flow management

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System.

Science.gov (United States)

Wu, Fan; Rüdiger, Christoph; Yuce, Mehmet Rasit

2017-02-01

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System

Directory of Open Access Journals (Sweden)

Fan Wu

2017-02-01

Full Text Available Wireless sensor networks (WSNs play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT, many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.

Ground-fault protection of insulated high-voltage power networks in mines

Energy Technology Data Exchange (ETDEWEB)

Pudelko, H

1976-09-01

Safety of power networks is discussed in underground black coal mines in Poland. Safety in mines with a long service life was compared with safety in mines constructed since 1950. Power networks and systems protecting against electric ground-faults in the 2 mine groups are comparatively evaluated. Systems for protection against electric ground-faults in mine high-voltage networks with an insulated star point of the transformer are characterized. Fluctuations of resistance of electrical insulation under conditions of changing load are analyzed. The results of analyses are given in 14 diagrams. Recommendations for design of systems protecting against electric ground-faults in 6 kV mine power systems are made. 7 references.

Identification of complex systems by artificial neural networks. Applications to mechanical frictions

International Nuclear Information System (INIS)

Dominguez, Manuel

1998-01-01

In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

Spectrum Reorganization and Bundling for Power Efficient Mobile Networks

DEFF Research Database (Denmark)

Micallef, Gilbert; Mogensen, Preben; Scheck, Hans-Otto

2012-01-01

are still required for supporting legacy devices and providing wider network coverage. In order to facilitate and reduce the cost of rolling out a new network, mobile operators often reuse existing sites. Radio frequency modules in base station sites house power amplifiers, which are designed to operate...

Finding Minimum-Power Broadcast Trees for Wireless Networks

Science.gov (United States)

Arabshahi, Payman; Gray, Andrew; Das, Arindam; El-Sharkawi, Mohamed; Marks, Robert, II

2004-01-01

Some algorithms have been devised for use in a method of constructing tree graphs that represent connections among the nodes of a wireless communication network. These algorithms provide for determining the viability of any given candidate connection tree and for generating an initial set of viable trees that can be used in any of a variety of search algorithms (e.g., a genetic algorithm) to find a tree that enables the network to broadcast from a source node to all other nodes while consuming the minimum amount of total power. The method yields solutions better than those of a prior algorithm known as the broadcast incremental power algorithm, albeit at a slightly greater computational cost.

Individual Identification Using Functional Brain Fingerprint Detected by Recurrent Neural Network.

Science.gov (United States)

Chen, Shiyang; Hu, Xiaoping P

2018-03-20

Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network based model for identifying individuals based on only a short segment of resting state functional MRI data. In addition, we demonstrate how the global signal and differences in atlases affect the individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.

Investigation of Hybrid Pseudo Bipolar HVDC Performances Supply Power to Passive AC Network

Directory of Open Access Journals (Sweden)

Kuan Li

2014-07-01

Full Text Available The traditional HVDC plays an important role in the development of power grid. But the traditional HVDC cannot supply power either to entirely passive AC network or to weak AC system. In fact, an entirely passive AC network can be effectively powered through VSC-HVDC. However, the cost of investment in VSC-HVDC is amazingly high due to the limitation of power electronics technology. Based on CSC and VSC, this paper proposes a method to build Hybrid HVDC, which makes the power supply to the passive AC network come true and, at the same time, lowers the investment cost. The effect of topology, steady mathematical model, startup characteristic, steady and transient characteristics in Hybrid HVDC system are systematically studied in this paper. The simulation result shows that Hybrid HVDC can supply power to the passive AC network with high stability. This study provides a theoretical basis for the further development of HVDC.

On the distribution and mean of received power in stochastic cellular network

OpenAIRE

Cao, Fengming; Ganesh, Ayalvadi; Armour, Simon; Sooriyabandara, Mahesh

2016-01-01

This paper exploits the distribution and mean of received power for cellular network with stochastic network modeling to study the difference between the two cell association criteria, i.e. the strongest received power based cell association and the closest distance based cell association. Consequently we derive the analytical expression of the distribution and the mean of the nth strongest received power and the received power from the nth nearest base station and the derivations have been c...

Behavioral Strategy: Strategic Consensus, Power and Networks

NARCIS (Netherlands)

M. Tarakci (Murat)

2013-01-01

textabstractOrganizations are embedded in a network of relationships and make sense of their business environment through the cognitive frames of their employees and executives who constantly experience battles for power. This dissertation integrates strategic management research with organizational

Village Building Identification Based on Ensemble Convolutional Neural Networks

Science.gov (United States)

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

Comparison Of Power Quality Disturbances Classification Based On Neural Network

Directory of Open Access Journals (Sweden)

Nway Nway Kyaw Win

2015-07-01

Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

More Opportunities than Wealth. A Network of Power and Frustration

Energy Technology Data Exchange (ETDEWEB)

Mahault, Benoit Alexandre [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Saxena, Avadh Behari [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Nisoli, Cristiano [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-08-17

We introduce a minimal agent-based model to qualitatively conceptualize the allocation of limited wealth among more abundant opportunities. We study the interplay of power, satisfaction and frustration in the problem of wealth distribution, concentration, and inequality. This framework allows us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from, or lose wealth to, anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity, only minimally ameliorated by disorder in a non-optimized society. The picture is however dramatically modified when hard constraints are imposed over agents, and they are forced to share wealth with neighbors on a network. We discuss the case of random networks and scale free networks. We then propose an out of equilibrium dynamics of the networks, based on a competition of power and frustration in the decision-making of agents that leads to network evolution. We show that the ratio of power and frustration controls different dynamical regimes separated by kinetic transition and characterized by drastically different values of the indices of equality.

Discrete rate and variable power adaptation for underlay cognitive networks

KAUST Repository

Abdallah, Mohamed M.; Salem, Ahmed H.; Alouini, Mohamed-Slim; Qaraqe, Khalid A.

2010-01-01

We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power

[Terahertz Spectroscopic Identification with Deep Belief Network].

Science.gov (United States)

Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

2015-12-01

Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

Mobile user forecast and power-law acceleration invariance of scale-free networks

International Nuclear Information System (INIS)

Guo Jin-Li; Guo Zhao-Hua; Liu Xue-Jiao

2011-01-01

This paper studies and predicts the number growth of China's mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well. (interdisciplinary physics and related areas of science and technology)

Using modular neural networks to monitor accident conditions in nuclear power plants

International Nuclear Information System (INIS)

Guo, Z.

1992-01-01

Nuclear power plants are very complex systems. The diagnoses of transients or accident conditions is very difficult because a large amount of information, which is often noisy, or intermittent, or even incomplete, need to be processed in real time. To demonstrate their potential application to nuclear power plants, neural networks axe used to monitor the accident scenarios simulated by the training simulator of TVA's Watts Bar Nuclear Power Plant. A self-organization network is used to compress original data to reduce the total number of training patterns. Different accident scenarios are closely related to different key parameters which distinguish one accident scenario from another. Therefore, the accident scenarios can be monitored by a set of small size neural networks, called modular networks, each one of which monitors only one assigned accident scenario, to obtain fast training and recall. Sensitivity analysis is applied to select proper input variables for modular networks

Estimation of Maximum Allowable PV Connection to LV Residential Power Networks

DEFF Research Database (Denmark)

Demirok, Erhan; Sera, Dezso; Teodorescu, Remus

2011-01-01

Maximum photovoltaic (PV) hosting capacity of low voltage (LV) power networks is mainly restricted by either thermal limits of network components or grid voltage quality resulted from high penetration of distributed PV systems. This maximum hosting capacity may be lower than the available solar...... potential of geographic area due to power network limitations even though all rooftops are fully occupied with PV modules. Therefore, it becomes more of an issue to know what exactly limits higher PV penetration level and which solutions should be engaged efficiently such as over sizing distribution...

Energy-efficient power control for OFDMA cellular networks

KAUST Repository

Sboui, Lokman

2016-12-24

In this paper, we study the energy efficiency (EE) of orthogonal frequency-division multiple access (OFDMA) cellular networks. Our objective is to present a power allocation scheme that maximizes the EE of downlink communications. We propose a novel explicit expression of the optimal power allocation to each subcarrier. We also present the power control when the transmit power is limited by power budget constraint or/and minimal rate constraint and we highlight the occurrence of some transmission outage events depending on the constraints\\' parameters. In the numerical results, we show that our proposed power control improves the EE especially at high power budget regime and low minimal rate regime. In addition, we show that having a higher number of subcarriers enhances the OFDMA EE.

Power-Controlled MAC Protocols with Dynamic Neighbor Prediction for Ad hoc Networks

Institute of Scientific and Technical Information of China (English)

LI Meng; ZHANG Lin; XIAO Yong-kang; SHAN Xiu-ming

2004-01-01

Energy and bandwidth are the scarce resources in ad hoc networks because most of the mobile nodes are battery-supplied and share the exclusive wireless medium. Integrating the power control into MAC protocol is a promising technique to fully exploit these precious resources of ad hoc wireless networks. In this paper, a new intelligent power-controlled Medium Access Control (MAC) (iMAC) protocol with dynamic neighbor prediction is proposed. Through the elaborate design of the distributed transmit-receive strategy of mobile nodes, iMAC greatly outperforms the prevailing IEEE 802.11 MAC protocols in not only energy conservation but also network throughput. Using the Dynamic Neighbor Prediction (DNP), iMAC performs well in mobile scenes. To the best of our knowledge, iMAC is the first protocol that considers the performance deterioration of power-controlled MAC protocols in mobile scenes and then proposes a solution. Simulation results indicate that DNP is important and necessary for power-controlled MAC protocols in mobile ad hoc networks.

Low Power Multi-Hop Networking Analysis in Intelligent Environments.

Science.gov (United States)

Etxaniz, Josu; Aranguren, Gerardo

2017-05-19

Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide.

The power grid AGC frequency bias coefficient online identification method based on wide area information

Science.gov (United States)

Wang, Zian; Li, Shiguang; Yu, Ting

2015-12-01

This paper propose online identification method of regional frequency deviation coefficient based on the analysis of interconnected grid AGC adjustment response mechanism of regional frequency deviation coefficient and the generator online real-time operation state by measured data through PMU, analyze the optimization method of regional frequency deviation coefficient in case of the actual operation state of the power system and achieve a more accurate and efficient automatic generation control in power system. Verify the validity of the online identification method of regional frequency deviation coefficient by establishing the long-term frequency control simulation model of two-regional interconnected power system.

Down-link power distributions for 2G and 3G mobile communication networks

International Nuclear Information System (INIS)

Colombi, D.; Thors, B.; Persson, T.; Wiren, N.; Larsson, L. E.; Jonsson, M.; Toernevik, C.

2013-01-01

Knowledge of realistic power levels is key when conducting accurate EMF exposure assessments. In this study, down-link output power distributions for radio base stations in 2G and 3G mobile communication networks have been assessed. The distributions were obtained from network measurement data collected from the Operations Support System, which normally is used for network monitoring and management. Significant amounts of data were gathered simultaneously for large sets of radio base stations covering wide geographical areas and different environments. The method was validated with in situ measurements. For the 3G network, the 90. percentile of the averaged output power during high traffic hours was found to be 43 % of the maximum available power. The corresponding number for 2G, with two or more transceivers installed, was 65 % or below. (authors)

Power-law behavior in complex organizational communication networks during crisis

Science.gov (United States)

Uddin, Shahadat; Murshed, Shahriar Tanvir Hasan; Hossain, Liaquat

2011-08-01

Communication networks can be described as patterns of contacts which are created due to the flow of messages and information shared among participating actors. Contemporary organizations are now commonly viewed as dynamic systems of adaptation and evolution containing several parts, which interact with one another both in internal and in external environment. Although there is limited consensus among researchers on the precise definition of organizational crisis, there is evidence of shared meaning: crisis produces individual crisis, crisis can be associated with positive or negative conditions, crises can be situations having been precipitated quickly or suddenly or situations that have developed over time and are predictable etc. In this research, we study the power-law behavior of an organizational email communication network during crisis from complexity perspective. Power law simply describes that, the probability that a randomly selected node has k links (i.e. degree k) follows P(k)∼k, where γ is the degree exponent. We used social network analysis tools and techniques to analyze the email communication dataset. We tested two propositions: (1) as organization goes through crisis, a few actors, who are prominent or more active, will become central, and (2) the daily communication network as well as the actors in the communication network exhibit power-law behavior. Our preliminary results support these two propositions. The outcome of this study may provide significant advancement in exploring organizational communication network behavior during crisis.

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.

Development of Light Powered Sensor Networks for Thermal Comfort Measurement

Directory of Open Access Journals (Sweden)

Dasheng Lee

2008-10-01

Full Text Available Recent technological advances in wireless communications have enabled easy installation of sensor networks with air conditioning equipment control applications. However, the sensor node power supply, through either power lines or battery power, still presents obstacles to the distribution of the sensing systems. In this study, a novel sensor network, powered by the artificial light, was constructed to achieve wireless power transfer and wireless data communications for thermal comfort measurements. The sensing node integrates an IC-based temperature sensor, a radiation thermometer, a relative humidity sensor, a micro machined flow sensor and a microprocessor for predicting mean vote (PMV calculation. The 935 MHz band RF module was employed for the wireless data communication with a specific protocol based on a special energy beacon enabled mode capable of achieving zero power consumption during the inactive periods of the nodes. A 5W spotlight, with a dual axis tilt platform, can power the distributed nodes over a distance of up to 5 meters. A special algorithm, the maximum entropy method, was developed to estimate the sensing quantity of climate parameters if the communication module did not receive any response from the distributed nodes within a certain time limit. The light-powered sensor networks were able to gather indoor comfort-sensing index levels in good agreement with the comfort-sensing vote (CSV preferred by a human being and the experimental results within the environment suggested that the sensing system could be used in air conditioning systems to implement a comfort-optimal control strategy.

[Research on identification of species of fruit trees by spectral analysis].

Science.gov (United States)

Xing, Dong-Xing; Chang, Qing-Rui

2009-07-01

Using the spectral reflectance data (R2) of canopies, the present paper identifies seven species of fruit trees bearing fruit in the fruit mature period. Firstly, it compares the fruit tree species identification capability of six kinds of satellite sensors and four kinds of vegetation index through re-sampling the spectral data with six kinds of pre-defined filter function and the related data processing of calculating vegetation indexes. Then, it structures a BP neural network model for identifying seven species of fruit trees on the basis of choosing the best transformation of R(lambda) and optimizing the model parameters. The main conclusions are: (1) the order of the identification capability of the six kinds of satellite sensors from strong to weak is: MODIS, ASTER, ETM+, HRG, QUICKBIRD and IKONOS; (2) among the four kinds of vegetation indexes, the identification capability of RVI is the most powerful, the next is NDVI, while the identification capability of SAVI or DVI is relatively weak; (3) The identification capability of RVI and NDVI calculated with the reflectance of near-infrared and red channels of ETM+ or MODIS sensor is relatively powerful; (4) Among R(lambda) and its 22 kinds of transformation data, d1 [log(1/R(lambda))](derivative gap is set 9 nm) is the best transformation for structuring BP neural network model; (5) The paper structures a 3-layer BP neural network model for identifying seven species of fruit trees using the best transformation of R(lambda) which is d1 [log(1/R(lambda))](derivative gap is set 9 nm).

Smart grids : combination of 'Virtual Power Plant'-concept and 'smart network'-design

NARCIS (Netherlands)

El Bakari, K.; Kling, W.L.

2010-01-01

The concept of virtual power plant (VPP) offers a solution to control and manage higher level of dispersed generation in nowadays passive distribution network. Under certain conditions the VPP is able to displace power and energy which implies more control on the energy flow in the networks. To

Real-time identification of residential appliance events based on power monitoring

Science.gov (United States)

Yang, Zhao; Zhu, Zhicheng; Wei, Zhiqiang; Yin, Bo; Wang, Xiuwei

2018-03-01

Energy monitoring for specific home appliances has been regarded as the pre-requisite for reducing residential energy consumption. To enhance the accuracy of identifying operation status of household appliances and to keep pace with the development of smart power grid, this paper puts forward the integration of electric current and power data on the basis of existing algorithm. If average power difference of several adjacent cycles varies from the baseline and goes beyond the pre-assigned threshold value, the event will be flagged. Based on MATLAB platform and domestic appliances simulations, the results of tested data and verified algorithm indicate that the power method has accomplished desired results of appliance identification.

Fuel number identification method and device

International Nuclear Information System (INIS)

Doi, Takami; Seno, Makoto; Kikuchi, Takashi; Sakamoto, Hiromi; Takahashi, Masaki; Tanaka, Keiji.

1997-01-01

The present invention provides a method of and a device for automatically identifying fuel numbers impressed on fuel assemblies disposed in a fuel reprocessing facility, power plant and a reactor core at a high speed and at a high identification rate. Namely, three or more character images are photographed for one fuel assembly as an object of the identification under different illumination conditions. As a result, different character images by the number of the illumination directions can be obtained for identical impressed characters. Learning on a neural network system is applied to the images of all of the characters impressed on the fuel assembly obtained under illumination of predetermined directions. Then, result of the identification by the number of the illumination directions can be obtained for each of the characters as an object of the identification. As a result, since the result of the identification is determined based on a theory of decision of majority, highly automatic identification can be realized. (I.S.)

Identification of initiating events using a master logic diagram in low-power and shutdown PSA for nuclear power plant

International Nuclear Information System (INIS)

Han, S. J.; Park, J. H.; Kim, T. W.; Ha, J. J.

2003-01-01

It is necessary to apply a formal technique instead of an empirical technique in the identification of initiating events for Low Power and ShutDown (LPSD) Probabilistic Safety Assessment (PSA) of Nuclear Power Plant (NPP). The present study focuses on the examination of Master Logic Diagram (MLD) technique as a formal technique in the identification of initiating events. The MLD technique is a deductive tool using top-down approach for the formal and logical indentification of initiating events. The present study modified the MLD used in the full power PSA considering the characteristics of LPSD operation. The modified MLD introduced a systematic formation in decomposition process of which the MLD for full power PSA lacked. The modified MLD was able to identify initiating events systematic and logical. However, the formal techniques including the MLD have a limitation for precisely identifying all of the initiating events. In order to overcome this limitation, it is necessary to combine it with an empirical technique. We expect that the modified MLD can be used in an upgrade of the current LPSD PSAs

Energy-Aware Sensor Networks via Sensor Selection and Power Allocation

KAUST Repository

Niyazi, Lama B.

2018-02-12

Finite energy reserves and the irreplaceable nature of nodes in battery-driven wireless sensor networks (WSNs) motivate energy-aware network operation. This paper considers energy-efficiency in a WSN by investigating the problem of minimizing the power consumption consisting of both radiated and circuit power of sensor nodes, so as to determine an optimal set of active sensors and corresponding transmit powers. To solve such a mixed discrete and continuous problem, the paper proposes various sensor selection and power allocation algorithms of low complexity. Simulation results show an appreciable improvement in their performance over a system in which no selection strategy is applied, with a slight gap from derived lower bounds. The results further yield insights into the relationship between the number of activated sensors and its effect on total power in different regimes of operation, based on which recommendations are made for which strategies to use in the different regimes.

Assessing the market power due to the network constraints in competitive electricity markets

International Nuclear Information System (INIS)

Bompard, E.; Ma, Y.C.; Napoli, R.; Jiang, C.W.

2006-01-01

The physical and operational constraints of the network pose very specific problems to market power analysis in the oligopolistic electricity markets. This paper presents a direct analytical approach to find the market equilibrium based on a supply function game model. The model is exploited to undertake a sensitivity analysis of the producer surplus with reference to the line flow limits under a DC power flow model for network representation. Two different kinds of indices, that can capture the market power arising under network constraints, are proposed. The first set of indices is the location privilege (LP), that measure the effect of the generators positioning in the grid on their surplus under perfect competition. The second set is for the network market power (NMP) indices that take into account the strategic behaviors of the producers that may take advantage of the congestion of the transmission lines. The indices allow for a ranking of the lines in terms of the market power they can induce and, in this respect, they may help the market regulator to focus on the network weakness in terms of the possible market outcomes under the market power behaviors from the supply side. The application of the proposed indices is illustrated with reference to the IEEE 30-bus test system. (author)

A Power Planning Algorithm Based on RPL for AMI Wireless Sensor Networks.

Science.gov (United States)

Miguel, Marcio L F; Jamhour, Edgard; Pellenz, Marcelo E; Penna, Manoel C

2017-03-25

The advanced metering infrastructure (AMI) is an architecture for two-way communication between electric, gas and water meters and city utilities. The AMI network is a wireless sensor network that provides communication for metering devices in the neighborhood area of the smart grid. Recently, the applicability of a routing protocol for low-power and lossy networks (RPL) has been considered in AMI networks. Some studies in the literature have pointed out problems with RPL, including sub-optimal path selection and instability. In this paper, we defend the viewpoint that careful planning of the transmission power in wireless RPL networks can significantly reduce the pointed problems. This paper presents a method for planning the transmission power in order to assure that, after convergence, the size of the parent set of the RPL nodes is as close as possible to a predefined size. Another important feature is that all nodes in the parent set offer connectivity through links of similar quality.

ISINA: INTEGRAL Source Identification Network Algorithm

Science.gov (United States)

Scaringi, S.; Bird, A. J.; Clark, D. J.; Dean, A. J.; Hill, A. B.; McBride, V. A.; Shaw, S. E.

2008-11-01

We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. First, we introduce the data set and the problems encountered when dealing with images obtained using the coded mask technique. The initial step of source candidate searching is introduced and an initial candidate list is created. A description of the feature extraction on the initial candidate list is then performed together with feature merging for these candidates. Three training and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky. Three independent random forests are built: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples. Based on observations with INTEGRAL, an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Spain), Czech Republic and Poland, and the participation of Russia and the USA. E-mail: simo@astro.soton.ac.uk

ONU power saving modes in next generation optical access networks: progress, efficiency and challenges.

Science.gov (United States)

Dixit, Abhishek; Lannoo, Bart; Colle, Didier; Pickavet, Mario; Demeester, Piet

2012-12-10

The optical network unit (ONU), installed at a customer's premises, accounts for about 60% of power in current fiber-to-the-home (FTTH) networks. We propose a power consumption model for the ONU and evaluate the ONU power consumption in various next generation optical access (NGOA) architectures. Further, we study the impact of the power savings of the ONU in various low power modes such as power shedding, doze and sleep.

Problem-Solving Methods for the Prospective Development of Urban Power Distribution Network

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2014-01-01

Full Text Available This article succeeds the former A. P. K nko’ and A. I. Kuzmina’ ubl t on titled "A mathematical model of urban distribution electro-network considering its future development" (electronic scientific and technical magazine "Science and education" No. 5, 2014.The article offers a model of urban power distribution network as a set of transformer and distribution substations and cable lines. All elements of the network and new consumers are determined owing to vectors of parameters consistent with them.A problem of the urban power distribution network design, taking into account a prospective development of the city, is presented as a problem of discrete programming. It is in deciding on the optimal option to connect new consumers to the power supply network, on the number and sites to build new substations, and on the option to include them in the power supply network.Two methods, namely a reduction method for a set the nested tasks of global minimization and a decomposition method are offered to solve the problem.In reduction method the problem of prospective development of power supply network breaks into three subtasks of smaller dimension: a subtask to define the number and sites of new transformer and distribution substations, a subtask to define the option to connect new consumers to the power supply network, and a subtask to include new substations in the power supply network. The vector of the varied parameters is broken into three subvectors consistent with the subtasks. Each subtask is solved using an area of admissible vector values of the varied parameters at the fixed components of the subvectors obtained when solving the higher subtasks.In decomposition method the task is presented as a set of three, similar to reduction method, reductions of subtasks and a problem of coordination. The problem of coordination specifies a sequence of the subtasks solution, defines the moment of calculation termination. Coordination is realized by

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Science.gov (United States)

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

A study on neural network representation of reactor power control procedures

International Nuclear Information System (INIS)

Moon, Byung Soo; Park, J. C.; Kim, Y. T.; Yang, S. U.; Lee, H. C.; Hwang, I. A.; Hwang, H. S.

1997-12-01

A neural algorithm to carry out the curve readings and arithmetic computations necessary for reactor power control is described in this report. The curve readings are for functions of the form z=f(x,y) and require fairly good interpolations. One of the functions is the total power defect as a function of reactor power and boron concentration. The second is the new position of control rod as a function of the current rod position and the increment of total power defect needed for the required power change. The curves involving xenon effect are also considered separately. We represented these curves by cubic spline interpolations first and then converted them to fuzzy systems so that they perform the identical interpolations as the splines. The resulting fuzzy systems are then converted to artificial neural networks similar to the RBF type neural network. These networks still carry the O(h'4) accuracy as the cubic spline interpolating functions. Also included is a description of an important result on how to find the spline interpolation coefficients without solving the matrix equation, when the function is a polynomial of the form f(t)=t'm. This result provides a systematic way of presenting continuous functions by fuzzy systems and hence by artificial neural networks without any training. (author). 10 refs., 2 tabs., 10 figs

Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

Directory of Open Access Journals (Sweden)

Ajay Kumar Saxena

2002-05-01

Full Text Available Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives postulated by various groups emphasized the need of an intelligent group decision support system approach in this field. In this paper, an Integrated Multimedia based Intelligent Group Decision Support System (IM1GDSS is presented, and its main components are analyzed and discussed. In particular attention is focused on the Data Base, Model Base, Central Black Board (CBB and Multicriteria Futuristic Decision Process (MFDP module. The model base interacts with Electrical Power Network Load Forecasting and Planning (EPNLFP Module; Resource Optimization, Modeling and Simulation (ROMAS Module; Electrical Power Network Control and Evaluation Process (EPNCAEP Module, and MFDP Module through CBB for strategic planning, management control, operational planning and transaction processing. The richness of multimedia channels adds a totally new dimension in a group decision making for Electrical Power Network. The proposed IMIGDSS is a user friendly, highly interactive group decision making system, based on efficient intelligent and multimedia communication support for group discussions, retrieval of content and multi criteria decision analysis.

Interconnection and power transportation networks: operation; Reseaux d'interconnexion et de transport: fonctionnement

Energy Technology Data Exchange (ETDEWEB)

Bornard, P. [Reseau de Transport d' Electricite (RTE), Div. Systeme Electrique, 92 - Paris la Defense (France); Pavard, M. [Electricite de France (EDF), 75 - Paris (France); Testud, G. [Reseau de Transport d' Electricite (RTE), Dept. Exploitation du Systeme Electrique, 92 - Paris la Defense (France)

2005-10-01

For economical or technical reasons, the power generation units are in general grouped together in the same geographical areas, while the consumption points are much more dispersed. The power transportation and interconnection networks ensure the link between the big generation areas and the big consumption areas, and also with other foreign networks. The network manager must keep the balance between the available offer and the potential demand and ensures the transit of energy between the production units and the end-users with respecting the contractual voltage and frequency ranges. Incidents must be mastered to limit the inconvenience to users and to avoid the complete collapse of the power transportation system. This article describes the operation of such systems: 1 - problems linked with networks operation; 2 - transits distribution: general problem, transmissible power in a power line, voltage drop, calculations of power distributions; 3 - voltage and frequency adjustments; 4 - operation in disturbed regime: mastery of common incidents, mechanisms of major incidents (operation of a power system in strongly disturbed regime), protection of the power system, recovery; 5 - conclusion. (J.S.)

Curing critical links in oscillator networks as power flow models

International Nuclear Information System (INIS)

Rohden, Martin; Meyer-Ortmanns, Hildegard; Witthaut, Dirk; Timme, Marc

2017-01-01

Modern societies crucially depend on the robust supply with electric energy so that blackouts of power grids can have far reaching consequences. Typically, large scale blackouts take place after a cascade of failures: the failure of a single infrastructure component, such as a critical transmission line, results in several subsequent failures that spread across large parts of the network. Improving the robustness of a network to prevent such secondary failures is thus key for assuring a reliable power supply. In this article we analyze the nonlocal rerouting of power flows after transmission line failures for a simplified AC power grid model and compare different strategies to improve network robustness. We identify critical links in the grid and compute alternative pathways to quantify the grid’s redundant capacity and to find bottlenecks along the pathways. Different strategies are developed and tested to increase transmission capacities to restore stability with respect to transmission line failures. We show that local and nonlocal strategies typically perform alike: one can equally well cure critical links by providing backup capacities locally or by extending the capacities of bottleneck links at remote locations. (paper)

Performance of wavelet analysis and neural networks for pathological voices identification

Science.gov (United States)

Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

2011-09-01

Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

Determination of power peak factor using control rods, ex-core detectors and neural networks

International Nuclear Information System (INIS)

Souza, Rose Mary Gomes do Prado

2005-01-01

This work presents a methodology based on the artificial neural network technique to predict in real time the power peak factor in a form that can be implemented in reactor protection systems. The neural network inputs were those available in the reactor protection systems, namely, the axial and quadrant power differences obtained from measured ex-core detector signals, and the position of control rods. The response of ex core detector signals was measured in experiments especially performed in the IPEN/MB-01 zero-power reactor. Several reactor states with different power density distribution were obtained by positioning the control rods in different configurations. The power distribution and its peak factor were calculated for each of these reactor states using the Citation code. The obtained results show that the power peak factor correlates well with the control rod position and the quadrant power difference, and with a lesser degree with the axial power differences. The data presented an inherent organisation and could be classified into different classes of power peak factor behaviour as a function of position of control rods, axial power difference and quadrant power difference. The RBF networks were able to identify classes and interpolate the power peak factor values. The relative error for the power peak factor estimation ranged from 0.19 % to 0.67 %, less than the one that was obtained performing a power density distribution map with in-core detectors. It was observed that the positions of control rods bear the detailed and localised information about the power density distribution, and that the axial and the quadrant power difference describe its global variations in the axial and radial directions. The results showed that the RBF and MLP networks produced similar results, and that a neural network correlation can be implemented in power reactor protection systems. (author)

Load power device and system for real-time execution of hierarchical load identification algorithms

Science.gov (United States)

Yang, Yi; Madane, Mayura Arun; Zambare, Prachi Suresh

2017-11-14

A load power device includes a power input; at least one power output for at least one load; and a plurality of sensors structured to sense voltage and current at the at least one power output. A processor is structured to provide real-time execution of: (a) a plurality of load identification algorithms, and (b) event detection and operating mode detection for the at least one load.

Power Efficient Service Differentiation Based on Traffic-Aware Survivable Elastic Optical Networks

DEFF Research Database (Denmark)

Turus, Ioan; Fagertun, Anna Manolova; Dittmann, Lars

2014-01-01

This study assesses the feasible energy savings whendefining different service classes based on protection schemesincore optical networks.Wepropose a dedicated energy savingstrategy for each of the service classes in order to minimize theoverall power consumption of the network.Four Classes of Se...... while for the proposed approach the difference in power consumption is almost negligible.Moreover, incase of the proposed approach,silver serviceclass can benefit for superior quality of service compared to the gold service class, due to the grooming mechanism.......This study assesses the feasible energy savings whendefining different service classes based on protection schemesincore optical networks.Wepropose a dedicated energy savingstrategy for each of the service classes in order to minimize theoverall power consumption of the network.Four Classes...... the sleep-mode capability of the opto-electronic devices as well as the elastic data-rateadaptation based on symbol-rate and modulation-format re-configurations. The results show that in the baseline approach the power consumption is strongly dependent on the ratio between the different service classes...

MicroRadarNet: A network of weather micro radars for the identification of local high resolution precipitation patterns

Science.gov (United States)

Turso, S.; Paolella, S.; Gabella, M.; Perona, G.

2013-01-01

In this paper, MicroRadarNet, a novel micro radar network for continuous, unattended meteorological monitoring is presented. Key aspects and constraints are introduced. Specific design strategies are highlighted, leading to the technological implementations of this wireless, low-cost, low power consumption sensor network. Raw spatial and temporal datasets are processed on-board in real-time, featuring a consistent evaluation of the signals from the sensors and optimizing the data loads to be transmitted. Network servers perform the final post-elaboration steps on the data streams coming from each unit. Final network products are meteorological mappings of weather events, monitored with high spatial and temporal resolution, and lastly served to the end user through any Web browser. This networked approach is shown to imply a sensible reduction of the overall operational costs, including management and maintenance aspects, if compared to the traditional long range monitoring strategy. Adoption of the TITAN storm identification and nowcasting engine is also here evaluated for in-loop integration within the MicroRadarNet data processing chain. A brief description of the engine workflow is provided, to present preliminary feasibility results and performance estimates. The outcomes were not so predictable, taking into account relevant operational differences between a Western Alps micro radar scenario and the long range radar context in the Denver region of Colorado. Finally, positive results from a set of case studies are discussed, motivating further refinements and integration activities.

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

International Nuclear Information System (INIS)

Souza, Rose Mary G.P.; Moreira, Joao M.L.

2006-01-01

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the

Analysis of Power Network for Line Reactance Variation to Improve Total Transmission Capacity

Directory of Open Access Journals (Sweden)

Ikram Ullah

2016-11-01

Full Text Available The increasing growth in power demand and the penetration of renewable distributed generations in competitive electricity market demands large and flexible capacity from the transmission grid to reduce transmission bottlenecks. The bottlenecks cause transmission congestion, reliability problems, restrict competition, and limit the maximum dispatch of low cost generations in the network. The electricity system requires efficient utilization of the current transmission capability to improve the Available Transfer Capability (ATC. To improve the ATC, power flow among the lines can be managed by using Flexible AC Transmission System (FACTS devices as power flow controllers, which alter the parameters of power lines. It is important to place FACTS devices on suitable lines to vary the reactance for improving Total Transmission Capacity (TTC of the network and provide flexibility in the power flow. In this paper a transmission network is analyzed based on line parameters variation to improve TTC of the interconnected system. Lines are selected for placing FACTS devices based on real power flow Performance Index (PI sensitivity factors. TTC is computed using the Repeated Power Flow (RPF method using the constraints of lines thermal limits, bus voltage limits and generator limits. The reactance of suitable lines, selected on the basis of PI sensitivity factors are changed to divert the power flow to other lines with enough transfer capacity available. The improvement of TTC using line reactance variation is demonstrated with three IEEE test systems with multi-area networks. The results show the variation of the selected lines’ reactance in improving TTC for all the test networks with defined contingency cases.

Network Theory Integrated Life Cycle Assessment for an Electric Power System

Directory of Open Access Journals (Sweden)

Heetae Kim

2015-08-01

Full Text Available In this study, we allocate Greenhouse gas (GHG emissions of electricity transmission to the consumers. As an allocation basis, we introduce energy distance. Energy distance takes the transmission load on the electricity energy system into account in addition to the amount of electricity consumption. As a case study, we estimate regional GHG emissions of electricity transmission loss in Chile. Life cycle assessment (LCA is used to estimate the total GHG emissions of the Chilean electric power system. The regional GHG emission of transmission loss is calculated from the total GHG emissions. We construct the network model of Chilean electric power grid as an undirected network with 466 nodes and 543 edges holding the topology of the power grid based on the statistical record. We analyze the total annual GHG emissions of the Chilean electricity energy system as 23.07 Mt CO2-eq. and 1.61 Mt CO2-eq. for the transmission loss, respectively. The total energy distance for the electricity transmission accounts for 12,842.10 TWh km based on network analysis. We argue that when the GHG emission of electricity transmission loss is estimated, the electricity transmission load should be separately considered. We propose network theory as a useful complement to LCA analysis for the complex allocation. Energy distance is especially useful on a very large-scale electric power grid such as an intercontinental transmission network.

A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks

Directory of Open Access Journals (Sweden)

Hyun-Ho Choi

2016-03-01

Full Text Available Most of the energy used to operate a cellular network is consumed by a base station (BS, and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases; in so doing, it significantly improves the energy efficiency of a cellular network.

Power variation and frequency regulation. Adaptation of PWR plant possibilities to the network needs

International Nuclear Information System (INIS)

Baboulin, J.P.; Burger, M.

1980-01-01

When the PWR are an important part of the power installed on a network, and that will be the case of the EDF network in the coming years, the participation of those plants to the power regulating becomes a necessity for the operating staff. This load regulating includes: daily variations of high amplitude; a permanent frequency - power regulating. The first part of the communication shows the network exploitation principles, and the resulting power variations concerning the existing nuclear power plants. Such transients are leading to stresses on fuel. The second part of the communication reports about the test program engaged by EDF in collaboration with the CEA and FRAMATOME, in order to study the fuel behaviour in real power conditions and power cycles, and that, just to the operational burn up of this fuel. (author)

Locality-Driven Parallel Static Analysis for Power Delivery Networks

KAUST Repository

Zeng, Zhiyu

2011-06-01

Large VLSI on-chip Power Delivery Networks (PDNs) are challenging to analyze due to the sheer network complexity. In this article, a novel parallel partitioning-based PDN analysis approach is presented. We use the boundary circuit responses of each partition to divide the full grid simulation problem into a set of independent subgrid simulation problems. Instead of solving exact boundary circuit responses, a more efficient scheme is proposed to provide near-exact approximation to the boundary circuit responses by exploiting the spatial locality of the flip-chip-type power grids. This scheme is also used in a block-based iterative error reduction process to achieve fast convergence. Detailed computational cost analysis and performance modeling is carried out to determine the optimal (or near-optimal) number of partitions for parallel implementation. Through the analysis of several large power grids, the proposed approach is shown to have excellent parallel efficiency, fast convergence, and favorable scalability. Our approach can solve a 16-million-node power grid in 18 seconds on an IBM p5-575 processing node with 16 Power5+ processors, which is 18.8X faster than a state-of-the-art direct solver. © 2011 ACM.

Transient identification system with noising data and 'don't know' response

International Nuclear Information System (INIS)

Mol, Antonio C. de A.; Martinez, Aquilino S.; Schirru, Roberto

2002-01-01

In the last years, many different approaches based on neural network (NN) has been proposed for transient identification in nuclear power plants (NPP). Some of them focus the dynamic identification using recurrent neural networks however, they are not able to deal with unrecognized transients. Other kind of solution uses competitive learning in order to allow the 'don't know' response. In this case dynamic, dynamic features are not well represented. This work presents a new approach for neural network based transient identification which allows either dynamic identification and 'don't know'response. Such approach uses two multilayer neural networks trained with backpropagation algorithm. The first one is responsible for the dynamic identification. This NN uses, a short set (in a movable time window) of recent measurements of each variable avoiding the necessity of using starting events. The other one is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide 'don't know' response. In order to validate the method a NPP transient identification problem comprising 15 postulated accidents, simulated for a pressurized water reactor, was proposed in the validation process it has been considered noising data in other to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. In order to validate the method, a NPP transient identification problem comprising 15 postulated accidents simulated for a pressurized water reactor, was proposed in the validation process it has been considered noising data in order to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. (author)

Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks.

Science.gov (United States)

Shi, Shengchao; Li, Guangxia; An, Kang; Gao, Bin; Zheng, Gan

2017-09-04

This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach's method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.

Delineating social network data anonymization via random edge perturbation

KAUST Repository

Xue, Mingqiang; Karras, Panagiotis; Raï ssi, Chedy; Kalnis, Panos; Pung, Hungkeng

2012-01-01

study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments

Development of neural network for analysis of local power distributions in BWR fuel bundles

International Nuclear Information System (INIS)

Tanabe, Akira; Yamamoto, Toru; Shinfuku, Kimihiro; Nakamae, Takuji.

1993-01-01

A neural network model has been developed to learn the local power distributions in a BWR fuel bundle. A two layers neural network with total 128 elements is used for this model. The neural network learns 33 cases of local power peaking factors of fuel rods with given enrichment distribution as the teacher signals, which were calculated by a fuel bundle nuclear analysis code based on precise physical models. This neural network model studied well the teacher signals within 1 % error. It is also able to calculate the local power distributions within several % error for the different enrichment distributions from the teacher signals when the average enrichment is close to 2 %. This neural network is simple and the computing speed of this model is 300 times faster than that of the precise nuclear analysis code. This model was applied to survey the enrichment distribution to meet a target local power distribution in a fuel bundle, and the enrichment distribution with flat power shape are obtained within short computing time. (author)

Method and system for conserving power in a telecommunications network during emergency situations

Energy Technology Data Exchange (ETDEWEB)

Conrad, Stephen H [Algodones, NM; O' Reilly, Gerard P [Manalapan, NJ

2011-10-11

Disclosed is a method and apparatus for conserving power in a telecommunications network during emergency situations. A permissible number list of emergency and/or priority numbers is stored in the telecommunications network. In the event of an emergency or power failure, input digits of a call to the telecommunications network are compared to the permissible number list. The call is processed in the telecommunications network and routed to its destination if the input digits match an entry in the permissible number list. The call is dropped without any further processing if the input digits do not match an entry in the permissible number list. Thus, power can be conserved in emergency situations by only allowing emergency and/or priority calls.

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Energy Technology Data Exchange (ETDEWEB)

Syed A. Ahmad; Umesh Shukla; Ryan E. Irwin; Luiz A. DaSilva; Allen B. MacKenzie

2011-03-01

In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five algorithms representative of DCPA used in literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate the effectiveness of the algorithms in achieving feasible link allocations in the network, as well as their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment (LICIPA) algorithm does not require cross-link gain information, has the overall lowest run time, and highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.

Parameter identification of a BWR nuclear power plant model for use in optimal control

International Nuclear Information System (INIS)

Volf, K.

1976-02-01

The problem being considered is the modeling of a nuclear power plant for the development of an optimal control system of the plant. Current system identification concepts, combining input/output information with a-priori structural information are employed. Two of the known parameter identification methods i.e., a least squares method and a maximum likelihood technique, are studied as ways of parameter identification from measurement data. A low order state variable stochastic model of a BWR nuclear power plant is presented as an application of this approach. The model consists of a deterministic and a noise part. The deterministic part is formed by simplified modeling of the major plant dynamic phenomena. The moise part models the effects of input random disturbances to the deterministic part and additive measurement noise. Most of the model parameters are assumed to be initially unknown. They are identified using measurement data records. A detailed high order digital computer simulation is used to simulate plant dynamic behaviour since it is not conceivable for experimentation of this kind to be performed on the real nuclear power plant. The identification task consists in adapting the performance of the simple model to the data acquired from this plant simulation ensuring the applicability of the techniques to measurement data acquired directly from the plant. (orig.) [de

Technical Survey on Applications of Wireless Sensor Networks in Nuclear Power Plants

International Nuclear Information System (INIS)

Jiang, Jin; Bari, Ataul; Chen, Dongyi; Hashemian, Hash M.

2014-01-01

Even though there is no general consensus on using wireless technologies in nuclear power plants, potential applications of wireless sensor networks within nuclear power plants (NPPs) has been investigated. The topics of interests include potential interaction of wireless sensor networks with the sensitive protection equipment, radiation damage of the electronics on board sensor nodes, optimal placement of relay nodes that collect and forward data in the network, and possible applications, such as radiation dose and level monitoring, and equipment condition monitoring. Several wireless sensor networks have been deployed on site of NPPs on a trial basis to perform these tasks. Different aspects of deployment of such wireless sensor networks in NPPs have also been examined. Industrial standards or guidelines for deployment of WSNs in NPPs are also been considered. This paper examines the state of the art of wireless sensor networks in NPPs

Technical Survey on Applications of Wireless Sensor Networks in Nuclear Power Plants

Energy Technology Data Exchange (ETDEWEB)

Jiang, Jin; Bari, Ataul [University of Western Ontario, Ontario (Canada); Chen, Dongyi [University of Electronic Science and Technology of China, Chengdu (China); Hashemian, Hash M. [AMS Technology Center, Knoxville (United States)

2014-08-15

Even though there is no general consensus on using wireless technologies in nuclear power plants, potential applications of wireless sensor networks within nuclear power plants (NPPs) has been investigated. The topics of interests include potential interaction of wireless sensor networks with the sensitive protection equipment, radiation damage of the electronics on board sensor nodes, optimal placement of relay nodes that collect and forward data in the network, and possible applications, such as radiation dose and level monitoring, and equipment condition monitoring. Several wireless sensor networks have been deployed on site of NPPs on a trial basis to perform these tasks. Different aspects of deployment of such wireless sensor networks in NPPs have also been examined. Industrial standards or guidelines for deployment of WSNs in NPPs are also been considered. This paper examines the state of the art of wireless sensor networks in NPPs.

Network fault response of wind power plants in distribution systems during reverse power flows. Part II

NARCIS (Netherlands)

Boemer, J.C.; Gibescu, M.; vd Meijden, M.A.M.M.; Rawn, B.G.; Kling, W.L.

2013-01-01

Abstract—The ability of wind power park modules to control their response to transmission network faults allows for specification of new control features directed at stabilising the power system response during and after disturbances. However, the ‘effectiveness’ of these features in situations

Identification of influential users by neighbors in online social networks

Science.gov (United States)

Sheikhahmadi, Amir; Nematbakhsh, Mohammad Ali; Zareie, Ahmad

2017-11-01

Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors' role for each user, a two-level method is proposed for both detecting users' influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

Use of neural networks to monitor power plant components

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.

1992-01-01

A new methodology is presented for nondestructive evaluation (NDE) of check valve performance and degradation. Artificial neural network (ANN) technology is utilized for processing frequency domain signatures of check valves operating in a nuclear power plant (NPP). Acoustic signatures obtained from different locations on a check valve are transformed from the time domain to the frequency domain and then used as input to a pretrained neural network. The neural network has been trained with data sets corresponding to normal operation, therefore establishing a basis for check valve satisfactory performance. Results obtained from the proposed methodology demonstrate the ability of neural networks to perform accurate and quick evaluations of check valve performance

Unavailability of critical SCADA communication links interconnecting a power grid and a Telco network

International Nuclear Information System (INIS)

Bobbio, A.; Bonanni, G.; Ciancamerla, E.; Clemente, R.; Iacomini, A.; Minichino, M.; Scarlatti, A.; Terruggia, R.; Zendri, E.

2010-01-01

The availability of power supply to power grid customers depends upon the availability of services of supervision, control and data acquisition (SCADA) system, which constitutes the nervous system of a power grid. In turn, SCADA services depend on the availability of the interconnected networks supporting such services. We propose a service oriented stochastic modelling methodology to investigate the availability of large interconnected networks, based on the hierarchical application of different modelling formalisms to different parts of the networks. Interconnected networks are decomposed according to the specific services delivered until the failure and repair mechanisms of the decomposed elementary blocks can be identified. We represent each network by a convenient stochastic modelling formalism, able to capture the main technological issues and to cope with realistic assumptions about failure and recovery mechanisms. This procedure confines the application of the more intensive computational techniques to those subsystems that actually require it. The paper concentrates on an actual failure scenario, occurred in Rome in January 2004 that involved the outage of critical SCADA communication links, interconnecting a power grid and a Telco network.

Unavailability of critical SCADA communication links interconnecting a power grid and a Telco network

Energy Technology Data Exchange (ETDEWEB)

Bobbio, A. [Dipartimento di Informatica, Universita del Piemonte Orientale, Viale Michel 11, 15121 Alessandria (Italy); Bonanni, G.; Ciancamerla, E. [ENEA - CRE Casaccia, Via Anguillarese 301, 00060 Roma (Italy); Clemente, R. [Telecom Italia Mobile, Via Isonzo112, 10141 Torino (Italy); Iacomini, A. [ACEA, Pl. Ostiense 2, 00154 Roma (Italy); Minichino, M., E-mail: minichino@casaccia.enea.i [ENEA - CRE Casaccia, Via Anguillarese 301, 00060 Roma (Italy); Scarlatti, A. [ACEA, Pl. Ostiense 2, 00154 Roma (Italy); Terruggia, R. [Dipartimento di Informatica, Universita del Piemonte Orientale, Viale Michel 11, 15121 Alessandria (Italy); Zendri, E. [ACEA, Pl. Ostiense 2, 00154 Roma (Italy)

2010-12-15

The availability of power supply to power grid customers depends upon the availability of services of supervision, control and data acquisition (SCADA) system, which constitutes the nervous system of a power grid. In turn, SCADA services depend on the availability of the interconnected networks supporting such services. We propose a service oriented stochastic modelling methodology to investigate the availability of large interconnected networks, based on the hierarchical application of different modelling formalisms to different parts of the networks. Interconnected networks are decomposed according to the specific services delivered until the failure and repair mechanisms of the decomposed elementary blocks can be identified. We represent each network by a convenient stochastic modelling formalism, able to capture the main technological issues and to cope with realistic assumptions about failure and recovery mechanisms. This procedure confines the application of the more intensive computational techniques to those subsystems that actually require it. The paper concentrates on an actual failure scenario, occurred in Rome in January 2004 that involved the outage of critical SCADA communication links, interconnecting a power grid and a Telco network.

Power Electronic Systems for Switched Reluctance Generator based Wind Farms and DC Networks

DEFF Research Database (Denmark)

Park, Kiwoo

enable various renewable energy sources, such as Photovoltaic (PV) and wind, to produce dc power directly. In addition, battery-based energy storage systems inherently operate with dc power. Hence, dc network (dc-grid) systems which connect these dc sources and storages directly using dc networks...... are gaining much attention again. The dc network system has a great potential to outdo the traditional ac systems in many technical challenges and could be highly profitable especially for offshore wind farm applications, where the size and weight of the components are crucial to the entire system costs......Wind power technology, as the most competitive renewable energy technology, is quickly developing. The wind turbine size is growing and the grid penetration of wind power is increasing rapidly. Recently, the developments on wind power technology pay more attentions on efficiency and reliability...

Securing Gateways within Clustered Power Centric Network of Nodes

Directory of Open Access Journals (Sweden)

Qaisar Javaid

2016-01-01

Full Text Available Knowledge Networks are gaining momentum within cyber world. Knowledge leads to innovation and for this reason organizations focus on research and information gathering in order to gain and improve existing knowledge. This of information era, which is primarily based on world wide web technologies, enables significantly expanded networks of people to communicate and collaborate 'virtually' across teams, across entire organizations and across the world, anytime and anywhere. Innovations in computing and telecommunications have transformed the corporations from structured and manageable types to interwoven network of blurred boundaries such as; ad hoc networks and mobile wireless networks, etc. This study explores knowledge networks in Information Technology and security leaks that are found, as well as measures that are taken to counter this menace which is coming up with optimal Secure Clustered Power Centric node network. The paper concludes these measures, evaluating and integrating them to come up with a secured network design.

Low-power cryptographic coprocessor for autonomous wireless sensor networks

Science.gov (United States)

Olszyna, Jakub; Winiecki, Wiesław

2013-10-01

The concept of autonomous wireless sensor networks involves energy harvesting, as well as effective management of system resources. Public-key cryptography (PKC) offers the advantage of elegant key agreement schemes with which a secret key can be securely established over unsecure channels. In addition to solving the key management problem, the other major application of PKC is digital signatures, with which non-repudiation of messages exchanges can be achieved. The motivation for studying low-power and area efficient modular arithmetic algorithms comes from enabling public-key security for low-power devices that can perform under constrained environment like autonomous wireless sensor networks. This paper presents a cryptographic coprocessor tailored to the autonomous wireless sensor networks constraints. Such hardware circuit is aimed to support the implementation of different public-key cryptosystems based on modular arithmetic in GF(p) and GF(2m). Key components of the coprocessor are described as GEZEL models and can be easily transformed to VHDL and implemented in hardware.

Coordinated system services from offshore wind power plants connected through HVDC networks

DEFF Research Database (Denmark)

Zeni, Lorenzo; Glasdam, Jakob; Hesselbæk, Bo

2014-01-01

This paper presents an overview of power system services in networks involving multiple onshore power systems, a voltage sourced converter (VSC) based high voltage direct current (HVDC) offshore network and an offshore wind power plant (OWPP). A comprehensive list of services regarding onshore...... as well as offshore network operation – both AC and DC – will be discussed from a state of the art perspective. Among them, the most interesting have been selected and will be treated in more detail and the main contribution of this paper will be to shed light on the most relevant aspects related...... to their implementation. For example, new findings on onshore AC voltage control are reported, that help the characterisation of potential AC voltage control that a VSC-HVDC station may offer to an onshore AC grid. The HVDC system behind the VSC-HVDC station may connect, through other converters, to another AC power...

A Distributed Routing Scheme for Energy Management in Solar Powered Sensor Networks

KAUST Repository

Dehwah, Ahmad H.

2017-10-11

Energy management is critical for solar-powered sensor networks. In this article, we consider data routing policies to optimize the energy in solar powered networks. Motivated by multipurpose sensor networks, the objective is to find the best network policy that maximizes the minimal energy among nodes in a sensor network, over a finite time horizon, given uncertain energy input forecasts. First, we derive the optimal policy in certain special cases using forward dynamic programming. We then introduce a greedy policy that is distributed and exhibits significantly lower complexity. When computationally feasible, we compare the performance of the optimal policy with the greedy policy. We also demonstrate the performance and computational complexity of the greedy policy over randomly simulated networks, and show that it yields results that are almost identical to the optimal policy, for greatly reduced worst-case computational costs and memory requirements. Finally, we demonstrate the implementation of the greedy policy on an experimental sensor network.

Discovering functional interdependence relationship in PPI networks for protein complex identification.

Science.gov (United States)

Lam, Winnie W M; Chan, Keith C C

2012-04-01

Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation

Power-Aware Intrusion Detection in Mobile Ad Hoc Networks

Science.gov (United States)

Şen, Sevil; Clark, John A.; Tapiador, Juan E.

Mobile ad hoc networks (MANETs) are a highly promising new form of networking. However they are more vulnerable to attacks than wired networks. In addition, conventional intrusion detection systems (IDS) are ineffective and inefficient for highly dynamic and resource-constrained environments. Achieving an effective operational MANET requires tradeoffs to be made between functional and non-functional criteria. In this paper we show how Genetic Programming (GP) together with a Multi-Objective Evolutionary Algorithm (MOEA) can be used to synthesise intrusion detection programs that make optimal tradeoffs between security criteria and the power they consume.

Dynamic baseline detection method for power data network service

Science.gov (United States)

Chen, Wei

2017-08-01

This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

Identification of chaotic systems by neural network with hybrid learning algorithm

International Nuclear Information System (INIS)

Pan, S.-T.; Lai, C.-C.

2008-01-01

Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

Robust synchronization of delayed neural networks based on adaptive control and parameters identification

International Nuclear Information System (INIS)

Zhou Jin; Chen Tianping; Xiang Lan

2006-01-01

This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

A lithology identification method for continental shale oil reservoir based on BP neural network

Science.gov (United States)

Han, Luo; Fuqiang, Lai; Zheng, Dong; Weixu, Xia

2018-06-01

The Dongying Depression and Jiyang Depression of the Bohai Bay Basin consist of continental sedimentary facies with a variable sedimentary environment and the shale layer system has a variety of lithologies and strong heterogeneity. It is difficult to accurately identify the lithologies with traditional lithology identification methods. The back propagation (BP) neural network was used to predict the lithology of continental shale oil reservoirs. Based on the rock slice identification, x-ray diffraction bulk rock mineral analysis, scanning electron microscope analysis, and the data of well logging and logging, the lithology was divided with carbonate, clay and felsic as end-member minerals. According to the core-electrical relationship, the frequency histogram was then used to calculate the logging response range of each lithology. The lithology-sensitive curves selected from 23 logging curves (GR, AC, CNL, DEN, etc) were chosen as the input variables. Finally, the BP neural network training model was established to predict the lithology. The lithology in the study area can be divided into four types: mudstone, lime mudstone, lime oil-mudstone, and lime argillaceous oil-shale. The logging responses of lithology were complicated and characterized by the low values of four indicators and medium values of two indicators. By comparing the number of hidden nodes and the number of training times, we found that the number of 15 hidden nodes and 1000 times of training yielded the best training results. The optimal neural network training model was established based on the above results. The lithology prediction results of BP neural network of well XX-1 showed that the accuracy rate was over 80%, indicating that the method was suitable for lithology identification of continental shale stratigraphy. The study provided the basis for the reservoir quality and oily evaluation of continental shale reservoirs and was of great significance to shale oil and gas exploration.

Neuro-diffuse algorithm for neutronic power identification of TRIGA Mark III reactor; Algoritmo neuro-difuso para la identificacion de la potencia neutronica del reactor Triga Mark III

Energy Technology Data Exchange (ETDEWEB)

Rojas R, E.; Benitez R, J. S. [Instituto Tecnologico de Toluca, Division de Estudios de Posgrado e Investigacion, Av. Tecnologico s/n, Ex-Rancho La Virgen, 50140 Metepec, Estado de Mexico (Mexico); Segovia de los Rios, J. A.; Rivero G, T. [ININ, Gerencia de Ciencias Aplicadas, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)], e-mail: jorge.benitez@inin.gob.mx

2009-10-15

In this work are presented the results of design and implementation of an algorithm based on diffuse logic systems and neural networks like method of neutronic power identification of TRIGA Mark III reactor. This algorithm uses the punctual kinetics equation as data generator of training, a cost function and a learning stage based on the descending gradient algorithm allow to optimize the parameters of membership functions of a diffuse system. Also, a series of criteria like part of the initial conditions of training algorithm are established. These criteria according to the carried out simulations show a quick convergence of neutronic power estimated from the first iterations. (Author)

Assessment of proactive transmission power control for wireless sensor networks

NARCIS (Netherlands)

kotian, Roshan; Exarchakos, Georgios; Liotta, Antonio

2014-01-01

In order to prolong lifetime of Wireless Sensor Networks (WSN), Transmission Power Control (TPC) techniques are employed. The existing TPC schemes adjust the transmission power mostly reacting to changes at link quality between communicating nodes. Proactive TPC has been proposed in the recent past

Cross-Layer Design Approach for Power Control in Mobile Ad Hoc Networks

Directory of Open Access Journals (Sweden)

A. Sarfaraz Ahmed

2015-03-01

Full Text Available In mobile ad hoc networks, communication among mobile nodes occurs through wireless medium The design of ad hoc network protocol, generally based on a traditional “layered approach”, has been found ineffective to deal with receiving signal strength (RSS-related problems, affecting the physical layer, the network layer and transport layer. This paper proposes a design approach, deviating from the traditional network design, toward enhancing the cross-layer interaction among different layers, namely physical, MAC and network. The Cross-Layer design approach for Power control (CLPC would help to enhance the transmission power by averaging the RSS values and to find an effective route between the source and the destination. This cross-layer design approach was tested by simulation (NS2 simulator and its performance over AODV was found to be better.

Power System Oscillation Modes Identifications: Guidelines for Applying TLS-ESPRIT Method

Science.gov (United States)

Gajjar, Gopal R.; Soman, Shreevardhan

2013-05-01

Fast measurements of power system quantities available through wide-area measurement systems enables direct observations for power system electromechanical oscillations. But the raw observations data need to be processed to obtain the quantitative measures required to make any inference regarding the power system state. A detailed discussion is presented for the theory behind the general problem of oscillatory mode indentification. This paper presents some results on oscillation mode identification applied to a wide-area frequency measurements system. Guidelines for selection of parametes for obtaining most reliable results from the applied method are provided. Finally, some results on real measurements are presented with our inference on them.

A neural network device for on-line particle identification in cosmic ray experiments

International Nuclear Information System (INIS)

Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.

2004-01-01

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

A Design of Wireless Sensor Networks for a Power Quality Monitoring System

Directory of Open Access Journals (Sweden)

Sanggil Kang

2010-11-01

Full Text Available Power grids deal with the business of generation, transmission, and distribution of electric power. Recently, interest in power quality in electrical distribution systems has increased rapidly. In Korea, the communication network to deliver voltage, current, and temperature measurements gathered from pole transformers to remote monitoring centers employs cellular mobile technology. Due to high cost of the cellular mobile technology, power quality monitoring measurements are limited and data gathering intervals are large. This causes difficulties in providing the power quality monitoring service. To alleviate the problems, in this paper we present a communication infrastructure to provide low cost, reliable data delivery. The communication infrastructure consists of wired connections between substations and monitoring centers, and wireless connections between pole transformers and substations. For the wireless connection, we employ a wireless sensor network and design its corresponding data forwarding protocol to improve the quality of data delivery. For the design, we adopt a tree-based data forwarding protocol in order to customize the distribution pattern of the power quality information. We verify the performance of the proposed data forwarding protocol quantitatively using the NS-2 network simulator.

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.

A design of wireless sensor networks for a power quality monitoring system.

Science.gov (United States)

Lim, Yujin; Kim, Hak-Man; Kang, Sanggil

2010-01-01

Power grids deal with the business of generation, transmission, and distribution of electric power. Recently, interest in power quality in electrical distribution systems has increased rapidly. In Korea, the communication network to deliver voltage, current, and temperature measurements gathered from pole transformers to remote monitoring centers employs cellular mobile technology. Due to high cost of the cellular mobile technology, power quality monitoring measurements are limited and data gathering intervals are large. This causes difficulties in providing the power quality monitoring service. To alleviate the problems, in this paper we present a communication infrastructure to provide low cost, reliable data delivery. The communication infrastructure consists of wired connections between substations and monitoring centers, and wireless connections between pole transformers and substations. For the wireless connection, we employ a wireless sensor network and design its corresponding data forwarding protocol to improve the quality of data delivery. For the design, we adopt a tree-based data forwarding protocol in order to customize the distribution pattern of the power quality information. We verify the performance of the proposed data forwarding protocol quantitatively using the NS-2 network simulator.

Low-power secure body area network for vital sensors toward IEEE802.15.6.

Science.gov (United States)

Kuroda, Masahiro; Qiu, Shuye; Tochikubo, Osamu

2009-01-01

Many healthcare/medical services have started using personal area networks, such as Bluetooth and ZigBee; these networks consist of various types of vital sensors. These works focus on generalized functions for sensor networks that expect enough battery capacity and low-power CPU/RF (Radio Frequency) modules, but less attention to easy-to-use privacy protection. In this paper, we propose a commercially-deployable secure body area network (S-BAN) with reduced computational burden on a real sensor that has limited RAM/ROM sizes and CPU/RF power consumption under a light-weight battery. Our proposed S-BAN provides vital data ordering among sensors that are involved in an S-BAN and also provides low-power networking with zero-administration security by automatic private key generation. We design and implement the power-efficient media access control (MAC) with resource-constraint security in sensors. Then, we evaluate the power efficiency of the S-BAN consisting of small sensors, such as an accessory type ECG and ring-type SpO2. The evaluation of power efficiency of the S-BAN using real sensors convinces us in deploying S-BAN and will also help us in providing feedbacks to the IEEE802.15.6 MAC, which will be the standard for BANs.

Joint Utility-Based Power Control and Receive Beamforming in Decentralized Wireless Networks

Directory of Open Access Journals (Sweden)

Angela Feistel

2010-01-01

Full Text Available This paper addresses the problem of joint resource allocation in general wireless networks and its practical implementation aspects. The objective is to allocate transmit powers and receive beamformers to the users in order to maximize a network-wide utility that represents the attained QoS and is a function of the signal-to-interference ratios. This problem is much more intricate than the corresponding QoS-based power control problem. In particular, it is not known which class of utility functions allows for a convex formulation of this problem. In case of perfect synchronization, the joint power and receiver control problem can be reformulated as a power control problem under optimal receivers. Standard gradient projection methods can be applied to solve this problem. However, these algorithms are not applicable in decentralized wireless networks. Therefore, we decompose the problem and propose a convergent alternate optimization that is amenable to distributed implementation. In addition, in real-world networks noisy measurements and estimations occur. Thus, the proposed algorithm has to be investigated in the framework of stochastic approximation. We discuss practical implementation aspects of the proposed stochastic algorithm and investigate its convergence properties by simulations.

The economic interplay between network and power market

International Nuclear Information System (INIS)

2001-01-01

The transmission network and the production plants are both necessary for providing electricity to the consumers. The transmission network is a regulated natural monopoly. The regulations set clear framework conditions for the network activities and affect the incentives to invest. Decision about operations and investments in production and consumption, on the other hand, are made in the market. There is a considerable need for coordination of the activities within network and market in order for the whole to work well. In the short term much of the need for coordination can be taken care of by a sophisticated organization of the power market and via the transmission tariffs, such that the players in the market see price signals which lead them to exploit network and production plants in an optimal way. This is complicated, but theoretically possible. In the long term, measures in the network, production and consumption are alternatives, and in development projects one often has to chose one of the alternatives. So far, no good models exist for guaranteeing the best choice

Scalable power selection method for wireless mesh networks

CSIR Research Space (South Africa)

Olwal, TO

2009-01-01

Full Text Available This paper addresses the problem of a scalable dynamic power control (SDPC) for wireless mesh networks (WMNs) based on IEEE 802.11 standards. An SDPC model that accounts for architectural complexities witnessed in multiple radios and hops...

Control of a hybrid compensator in a power network by an artificial neural network

Directory of Open Access Journals (Sweden)

I. S. Shaw

1998-07-01

Full Text Available Increased interest in the elimination of distortion in electrical power networks has led to the development of various compensator topologies. The increasing cost of electrical energy necessitates the cost-effective operation of any of these topologies. This paper considers the development of an artificial neural network based controller, trained by means of the backpropagation method, that ensures the cost-effective operation of the hybrid compensator consisting of various converters and filters.

Visiting Power Laws in Cyber-Physical Networking Systems

Directory of Open Access Journals (Sweden)

Ming Li

2012-01-01

Full Text Available Cyber-physical networking systems (CPNSs are made up of various physical systems that are heterogeneous in nature. Therefore, exploring universalities in CPNSs for either data or systems is desired in its fundamental theory. This paper is in the aspect of data, aiming at addressing that power laws may yet be a universality of data in CPNSs. The contributions of this paper are in triple folds. First, we provide a short tutorial about power laws. Then, we address the power laws related to some physical systems. Finally, we discuss that power-law-type data may be governed by stochastically differential equations of fractional order. As a side product, we present the point of view that the upper bound of data flow at large-time scaling and the small one also follows power laws.

The computer simulation of the resonant network for the B-factory model power supply

International Nuclear Information System (INIS)

Zhou, W.; Endo, K.

1993-07-01

A high repetition model power supply and the resonant magnet network are simulated with the computer in order to check and improve the design of the power supply for the B-factory booster. We put our key point on a transient behavior of the power supply and the resonant magnet network. The results of the simulation are given. (author)

Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

OpenAIRE

Ajay Kumar Saxena; S. 0. Bhatnagar; P. K Saxena

2002-01-01

Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives p...

Identification of Successive ``Unobservable'' Cyber Data Attacks in Power Systems Through Matrix Decomposition

Science.gov (United States)

Gao, Pengzhi; Wang, Meng; Chow, Joe H.; Ghiocel, Scott G.; Fardanesh, Bruce; Stefopoulos, George; Razanousky, Michael P.

2016-11-01

This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting "unobservable" cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.

Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network

Directory of Open Access Journals (Sweden)

Xia-an Bi

2015-01-01

Full Text Available With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.

Robust Power Supply Restoration for Self-Healing Active Distribution Networks Considering the Availability of Distributed Generation

Directory of Open Access Journals (Sweden)

Qiang Yang

2018-01-01

Full Text Available The increasing penetration of distributed generations (DGs with intermittent and stochastic characteristics into current power distribution networks can lead to increased fault levels and degradation in network protection. As one of the key requirements of active network management (ANM, efficient power supply restoration solution to guarantee network self-healing capability with full consideration of DG uncertainties is demanded. This paper presents a joint power supply restoration through combining the DG local restoration and switcher operation-based restoration to enhance the self-healing capability in active distribution networks considering the availability of distributed generation. The restoration algorithmic solution is designed to be able to carry out power restoration in parallel upon multiple simultaneous faults to maximize the load restoration while additionally minimizing power loss, topology variation and power flow changes due to switcher operations. The performance of the proposed solution is validated based on a 53-bus distribution network with wind power generators through extensive simulation experiments for a range of fault cases and DG scenarios generated based on Heuristic Moment Matching (HMM method to fully consider the DG randomness. The numerical result in comparison with the existing solutions demonstrates the effectiveness of the proposed power supply restoration solution.

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

Science.gov (United States)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

Adiabatic superconducting cells for ultra-low-power artificial neural networks

Directory of Open Access Journals (Sweden)

Andrey E. Schegolev

2016-10-01

Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

Power to Detect Intervention Effects on Ensembles of Social Networks

Science.gov (United States)

Sweet, Tracy M.; Junker, Brian W.

2016-01-01

The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention…

Comparative analysis of the application of different Low Power Wide Area Network technologies in power grid

Science.gov (United States)

Wang, Hao; Sui, Hong; Liao, Xing; Li, Junhao

2018-03-01

Low Power Wide Area Network (LPWAN) technologies developed rapidly in recent years, but the application principle of different LPWAN technologies in power grid is still not clear. This paper gives a comparative analysis of two mainstream LPWAN technologies including NB-IoT and LoRa, and gives an application suggestion of these two LPWAN technologies, which can guide the planning and construction of LPWAN in power grid.

Potential applications of neural networks to nuclear power plants

International Nuclear Information System (INIS)

Uhrig, R.E.

1991-01-01

Application of neural networks to the operation of nuclear power plants is being investigated under a US Department of Energy sponsored program at the University of Tennessee. Projects include the feasibility of using neural networks for the following tasks: diagnosing specific abnormal conditions, detection of the change of mode of operation, signal validation, monitoring of check valves, plant-wide monitoring using autoassociative neural networks, modeling of the plant thermodynamics, emulation of core reload calculations, monitoring of plant parameters, and analysis of plant vibrations. Each of these projects and its status are described briefly in this article. The objective of each of these projects is to enhance the safety and performance of nuclear plants through the use of neural networks

Artificial Neural Network In Maximum Power Point Tracking Algorithm Of Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Modestas Pikutis

2014-05-01

Full Text Available Scientists are looking for ways to improve the efficiency of solar cells all the time. The efficiency of solar cells which are available to the general public is up to 20%. Part of the solar energy is unused and a capacity of solar power plant is significantly reduced – if slow controller or controller which cannot stay at maximum power point of solar modules is used. Various algorithms of maximum power point tracking were created, but mostly algorithms are slow or make mistakes. In the literature more and more oftenartificial neural networks (ANN in maximum power point tracking process are mentioned, in order to improve performance of the controller. Self-learner artificial neural network and IncCond algorithm were used for maximum power point tracking in created solar power plant model. The algorithm for control was created. Solar power plant model is implemented in Matlab/Simulink environment.

Lifetime Maximizing Adaptive Power Control in Wireless Sensor Networks

National Research Council Canada - National Science Library

Sun, Fangting; Shayman, Mark

2006-01-01

...: adaptive power control. They focus on the sensor networks that consist of a sink and a set of homogeneous wireless sensor nodes, which are randomly deployed according to a uniform distribution...

Optimization of Multiresonant Wireless Power Transfer Network Based on Generalized Coupled Matrix

Directory of Open Access Journals (Sweden)

Qiang Zhao

2017-01-01

Full Text Available Magnetic coupling resonant wireless power transfer network (MCRWPTN system can realize wireless power transfer for some electrical equipment real-time and high efficiency in a certain spatial scale, which resolves the contradiction between power transfer efficiency and the power transfer distance of the wireless power transfer. A fully coupled resonant energy transfer model for multirelay coils and ports is established. A dynamic adaptive impedance matching control based on fully coupling matrix and particle swarm optimization algorithm based on annealing is developed for the MCRWPTN. Furthermore, as an example, the network which has twenty nodes is analyzed, and the best transmission coefficient which has the highest power transfer efficiency is found using the optimization algorithm, and the coupling constraints are considered simultaneously. Finally, the effectiveness of the proposed method is proved by the simulation results.

Power Saving Scheduling Scheme for Internet of Things over LTE/LTE-Advanced Networks

OpenAIRE

Kuo, Yen-Wei; Chou, Li-Der

2015-01-01

The devices of Internet of Things (IoT) will grow rapidly in the near future, and the power consumption and radio spectrum management will become the most critical issues in the IoT networks. Long Term Evolution (LTE) technology will become a promising technology used in IoT networks due to its flat architecture, all-IP network, and greater spectrum efficiency. The 3rd Generation Partnership Project (3GPP) specified the Discontinuous Reception (DRX) to reduce device’s power consumption. Howev...

Evaluation of the Harmonic Impedance of Frydlant (Line 53 Power Network

Directory of Open Access Journals (Sweden)

Shehab Abdulwadood Ali

2013-01-01

Full Text Available The harmonic impedance of the power system is an important quantity, which describes the behaviour of the power system with the existence of different frequencies beside the fundamental. The harmonic study belongs to the most difficult part of the electrical measurements and calculations even by using computer software. The main problem accompanying the calculating methods is to obtain the exact data of the electrical network parameters or to know the exact operational status of the system. In this paper the harmonic impedance of Frydlant (line 53, 22~kV in Czech Republic power network was calculated by using a very specialized software and suitable for solving such calculations called "NetCalc. version 3.0" [1].

Efficient Approach for Harmonic Resonance Identification of Large Wind Power Plants

DEFF Research Database (Denmark)

Ebrahimzadeh, Esmaeil; Blaabjerg, Frede; Wang, Xiongfei

2016-01-01

Unlike conventional power systems where the resonance frequencies are mainly determined by the passive components parameters, large Wind Power Plants (WPPs) may introduce additional harmonic resonances because of the interactions of the wideband control systems of power converters with each other...... and with passive components. This paper presents an efficient approach for identification of harmonic resonances in large WPPs containing power electronic converters, cable, transformer, capacitor banks, shunt reactors, etc. The proposed approach introduces a large WPP as a Multi-Input Multi-Output (MIMO) control...... system by considering the linearized models of the inner control loops of grid-side converters. Therefore, the resonance frequencies of the WPP resulting from passive components and the control loop interactions are identified based on the determinant of the transfer function matrix of the introduced...

Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

Science.gov (United States)

Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

2013-02-01

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

Jimena: efficient computing and system state identification for genetic regulatory networks.

Science.gov (United States)

Karl, Stefan; Dandekar, Thomas

2013-10-11

Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

Probability of islanding in utility networks due to grid connected photovoltaic power systems

Energy Technology Data Exchange (ETDEWEB)

Verhoeven, B.

2002-09-15

This report for the International Energy Agency (IEA) made by Task 5 of the Photovoltaic Power Systems (PVPS) programme takes a look at the probability of islanding in utility networks due to grid-connected photovoltaic power systems. The mission of the Photovoltaic Power Systems Programme is to enhance the international collaboration efforts which accelerate the development and deployment of photovoltaic solar energy. Task 5 deals with issues concerning grid-interconnection and distributed PV power systems. This report summarises the results on a study on the probability of islanding in power networks with a high penetration level of grid connected PV-systems. The results are based on measurements performed during one year in a Dutch utility network. The measurements of active and reactive power were taken every second for two years and stored in a computer for off-line analysis. The area examined and its characteristics are described, as are the test set-up and the equipment used. The ratios between load and PV-power are discussed. The general conclusion is that the probability of islanding is virtually zero for low, medium and high penetration levels of PV-systems.

Transmission Power Control for Wireless Sensor Network

Directory of Open Access Journals (Sweden)

Kuo-Hsien Hsia

2017-02-01

Full Text Available Wireless sensor networks can be widely applied for a security system or a smart home system. Since some of the wireless remote sensor nodes may be powered by energy storage devices such as batteries, it is a very important issue to transmit signals at lower power with the consideration of the communication effectiveness. In this paper, we will provide a fuzzy controller with two inputs and one output for received signal strength indicator (RSSI and link quality indicator (LQI to adjust transmission power suitably in order to maintaining a certain communication level with a reduced energy consumption. And we will divide the sampling period of a sensor node into four intervals so that the sensor node radio device does not in receiving or transmission status all the time. Hence the sensor node can adjust transmission power automatically and reduce sensor node power consumption. Experimental results show that the battery life can be extended to about 10 times for the designed sensor node comparing to a normal node.

Hierarchically Coordinated Power Management for Target Tracking in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Feng Juan

2013-10-01

Full Text Available Energy efficiency is very important for wireless sensor networks (WSNs since sensor nodes have a limited energy supply from a battery. So far, a lot research has focused on this issue, while less emphasis has been placed on the adaptive sleep time for each node with a consideration for the application constraints. In this paper, we propose a hierarchically coordinated power management (HCPM approach, which both addresses the energy conservation problem and reduces the packet forwarding delay for target tracking WSNs based on a virtual-grid-based network structure. We extend the network lifetime by adopting an adaptive sleep scheduling scheme that combines the local power management (PM and the adaptive coordinate PM strategies to schedule the activities of the sensor nodes at the surveillance stage. Furthermore, we propose a hierarchical structure for the tracking stage. Experimental results show that the proposed approach has a greater capability of extending the network lifetime while maintaining a short transmission delay when compared with the protocol which does not consider the application constraints in target tracking sensor networks.

Application of OLAM network in X-ray spectral analysis

International Nuclear Information System (INIS)

Liu Yinbing; Zhou Rongsheng

2001-01-01

The author describes a new approach to the automatic radioisotope identification problem based on the use of OLAM network. Different from the traditional methods, the OLAM network takes the spectrum as a whole comparing its shape with the patterns learned during the training period of the network. It is found that the OLAM network, once adequately trained, is quite suitable to identify a given isotope present in a mixture of elements as well as the relative proportions of each identified substance. Preliminary results are good enough to consider OLAM network as powerful and simple tools in the automatic spectrum analysis

Neural network for adapting nuclear power plant control for wide-range operation

International Nuclear Information System (INIS)

Ku, C.C.; Lee, K.Y.; Edwards, R.M.

1991-01-01

A new concept of using neural networks has been evaluated for optimal control of a nuclear reactor. The neural network uses the architecture of a standard backpropagation network; however, a new dynamic learning algorithm has been developed to capture the underlying system dynamics. The learning algorithm is based on parameter estimation for dynamic systems. The approach is demonstrated on an optimal reactor temperature controller by adjusting the feedback gains for wide-range operation. Application of optimal control to a reactor has been considered for improving temperature response using a robust fifth-order reactor power controller. Conventional gain scheduling can be employed to extend the range of good performance to accommodate large changes in power where nonlinear characteristics significantly modify the dynamics of the power plant. Gain scheduling is developed based on expected parameter variations, and it may be advantageous to further adapt feedback gains on-line to better match actual plant performance. A neural network approach is used here to adapt the gains to better accommodate plant uncertainties and thereby achieve improved robustness characteristics

A Kalman-filter based approach to identification of time-varying gene regulatory networks.

Directory of Open Access Journals (Sweden)

Jie Xiong

Full Text Available MOTIVATION: Conventional identification methods for gene regulatory networks (GRNs have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. RESULTS: It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.

A 500-600 MHz GaN power amplifier with RC-LC stability network

Science.gov (United States)

Ma, Xinyu; Duan, Baoxing; Yang, Yintang

2017-08-01

A 500-600 MHz high-efficiency, high-power GaN power amplifier is designed and realized on the basis of the push-pull structure. The RC-LC stability network is proposed and applied to the power amplifier circuit for the first time. The RC-LC stability network can significantly reduce the high gain out the band, which eliminates the instability of the power amplifier circuit. The developed power amplifier exhibits 58.5 dBm (700 W) output power with a 17 dB gain and 85% PAE at 500-600 MHz, 300 μs, 20% duty cycle. It has the highest PAE in P-band among the products at home and abroad. Project supported by the National Key Basic Research Program of China (No. 2014CB339901).

Transmit Power Optimisation in Wireless Network

Directory of Open Access Journals (Sweden)

Besnik Terziu

2011-09-01

Full Text Available Transmit power optimisation in wireless networks based on beamforming have emerged as a promising technique to enhance the spectrum efficiency of present and future wireless communication systems. The aim of this study is to minimise the access point power consumption in cellular networks while maintaining a targeted quality of service (QoS for the mobile terminals. In this study, the targeted quality of service is delivered to a mobile station by providing a desired level of Signal to Interference and Noise Ratio (SINR. Base-stations are coordinated across multiple cells in a multi-antenna beamforming system. This study focuses on a multi-cell multi-antenna downlink scenario where each mobile user is equipped with a single antenna, but where multiple mobile users may be active simultaneously in each cell and are separated via spatial multiplexing using beamforming. The design criteria is to minimize the total weighted transmitted power across the base-stations subject to SINR constraints at the mobile users. The main contribution of this study is to define an iterative algorithm that is capable of finding the joint optimal beamformers for all basestations, based on a correlation-based channel model, the full-correlation model. Among all correlated channel models, the correlated channel model used in this study is the most accurate, giving the best performance in terms of power consumption. The environment here in this study is chosen to be Non-Light of- Sight (NLOS condition, where a signal from a wireless transmitter passes several obstructions before arriving at a wireless receiver. Moreover there are many scatterers local to the mobile, and multiple reflections can occur among them before energy arrives at the mobile. The proposed algorithm is based on uplink-downlink duality using the Lagrangian duality theory. Time-Division Duplex (TDD is chosen as the platform for this study since it has been adopted to the latest technologies in Fourth

Orientation selective neural network for cosmic muon identification

International Nuclear Information System (INIS)

Abramowicz, H.; Tel Aviv Univ.; Horn, D.; Naftaly, U.; Sahar-Pikielny, C.

1997-01-01

We discuss a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Motivated by principles employed by the visual cortex, we construct orientation selective neurons in a neural network that performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons that leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices. (orig.)

Coordinated Scheduling and Power Control in Cloud-Radio Access Networks

KAUST Repository

Douik, Ahmed

2015-12-01

This paper addresses the joint coordinated scheduling and power control problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy, power control, and synchronization of the transmit frames across the single-antenna base-stations (BS). The transmit frame consists of several time/frequency blocks, called power-zones (PZ). The paper considers the problem of scheduling users to PZs and determining their power levels (PL), by maximizing the weighted sum-rate under the practical constraints that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the joint scheduling and power control graph formed by several clusters, where each is formed by a set of vertices, representing the possible association of users, BSs, and PLs for one specific PZ. The problem is, then, formulated as a maximumweight clique problem, in which the weight of each vertex is the sum of the benefits of the individual associations belonging to that vertex. Simulation results suggest that the proposed crosslayer scheme provides appreciable performance improvement as compared to schemes from recent literature.

Coordinated Scheduling and Power Control in Cloud-Radio Access Networks

KAUST Repository

Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2015-01-01

This paper addresses the joint coordinated scheduling and power control problem in cloud-enabled networks. Consider the downlink of a cloud-radio access network (CRAN), where the cloud is only responsible for the scheduling policy, power control, and synchronization of the transmit frames across the single-antenna base-stations (BS). The transmit frame consists of several time/frequency blocks, called power-zones (PZ). The paper considers the problem of scheduling users to PZs and determining their power levels (PL), by maximizing the weighted sum-rate under the practical constraints that each user cannot be served by more than one base-station, but can be served by one or more power-zones within each base-station frame. The paper solves the problem using a graph theoretical approach by introducing the joint scheduling and power control graph formed by several clusters, where each is formed by a set of vertices, representing the possible association of users, BSs, and PLs for one specific PZ. The problem is, then, formulated as a maximumweight clique problem, in which the weight of each vertex is the sum of the benefits of the individual associations belonging to that vertex. Simulation results suggest that the proposed crosslayer scheme provides appreciable performance improvement as compared to schemes from recent literature.

Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

DEFF Research Database (Denmark)

Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted

1998-01-01

A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...

The application of PSA techniques to the vital area identification of nuclear power plants

International Nuclear Information System (INIS)

Ha, Jae Joo; Jung, Woo Sik; Park, Chang Kue

2005-01-01

This paper presents a Vital Area Identification (VAI) method based on the current Fault Tree Analysis (FTA) and Probabilistic Safety Assessment (PSA) techniques for the physical protection of nuclear power plants. A structured framework of a Top Event Prevention set Analysis (TEPA) application to the VAI of nuclear power plants is also delineated. One of the important processes for physical protection in a nuclear power plant is VIA that is a process for identifying areas containing nuclear materials, Structures, Systems or Components (SSCs) to be protected from sabotage, which could directly or indirectly lead to core damage and unacceptable radiological consequences. A software VIP (Vital area Identification Package based on the PSA method) is being developed by KAERI for the VAI of nuclear power plants. Furthermore, the KAERI fault tree solver FTREX (Fault Tree Reliability Evaluation eXpert) is specialized for the VIP to generate the candidates of the vital areas. FTREX can generate numerous MCSs for a huge fault tree with the lowest truncation limit and all possible prevention sets

Hazard Identification, Risk Assessment and Risk Control (HIRARC Accidents at Power Plant

Directory of Open Access Journals (Sweden)

Ahmad Asmalia Che

2016-01-01

Full Text Available Power plant had a reputation of being one of the most hazardous workplace environments. Workers in the power plant face many safety risks due to the nature of the job. Although power plants are safer nowadays since the industry has urged the employer to improve their employees’ safety, the employees still stumble upon many hazards thus accidents at workplace. The aim of the present study is to investigate work related accidents at power plants based on HIRARC (Hazard Identification, Risk Assessment and Risk Control process. The data were collected at two coal-fired power plant located in Malaysia. The finding of the study identified hazards and assess risk relate to accidents occurred at the power plants. The finding of the study suggested the possible control measures and corrective actions to reduce or eliminate the risk that can be used by power plant in preventing accidents from occurred

Self-Coexistence among IEEE 802.22 Networks: Distributed Allocation of Power and Channel.

Science.gov (United States)

Sakin, Sayef Azad; Razzaque, Md Abdur; Hassan, Mohammad Mehedi; Alamri, Atif; Tran, Nguyen H; Fortino, Giancarlo

2017-12-07

Ensuring self-coexistence among IEEE 802.22 networks is a challenging problem owing to opportunistic access of incumbent-free radio resources by users in co-located networks. In this study, we propose a fully-distributed non-cooperative approach to ensure self-coexistence in downlink channels of IEEE 802.22 networks. We formulate the self-coexistence problem as a mixed-integer non-linear optimization problem for maximizing the network data rate, which is an NP-hard one. This work explores a sub-optimal solution by dividing the optimization problem into downlink channel allocation and power assignment sub-problems. Considering fairness, quality of service and minimum interference for customer-premises-equipment, we also develop a greedy algorithm for channel allocation and a non-cooperative game-theoretic framework for near-optimal power allocation. The base stations of networks are treated as players in a game, where they try to increase spectrum utilization by controlling power and reaching a Nash equilibrium point. We further develop a utility function for the game to increase the data rate by minimizing the transmission power and, subsequently, the interference from neighboring networks. A theoretical proof of the uniqueness and existence of the Nash equilibrium has been presented. Performance improvements in terms of data-rate with a degree of fairness compared to a cooperative branch-and-bound-based algorithm and a non-cooperative greedy approach have been shown through simulation studies.

Self-Coexistence among IEEE 802.22 Networks: Distributed Allocation of Power and Channel

Directory of Open Access Journals (Sweden)

Sayef Azad Sakin

2017-12-01

Full Text Available Ensuring self-coexistence among IEEE 802.22 networks is a challenging problem owing to opportunistic access of incumbent-free radio resources by users in co-located networks. In this study, we propose a fully-distributed non-cooperative approach to ensure self-coexistence in downlink channels of IEEE 802.22 networks. We formulate the self-coexistence problem as a mixed-integer non-linear optimization problem for maximizing the network data rate, which is an NP-hard one. This work explores a sub-optimal solution by dividing the optimization problem into downlink channel allocation and power assignment sub-problems. Considering fairness, quality of service and minimum interference for customer-premises-equipment, we also develop a greedy algorithm for channel allocation and a non-cooperative game-theoretic framework for near-optimal power allocation. The base stations of networks are treated as players in a game, where they try to increase spectrum utilization by controlling power and reaching a Nash equilibrium point. We further develop a utility function for the game to increase the data rate by minimizing the transmission power and, subsequently, the interference from neighboring networks. A theoretical proof of the uniqueness and existence of the Nash equilibrium has been presented. Performance improvements in terms of data-rate with a degree of fairness compared to a cooperative branch-and-bound-based algorithm and a non-cooperative greedy approach have been shown through simulation studies.

Optimal Power Flow for resistive DC Network : A Port-Hamiltonian approach

NARCIS (Netherlands)

Benedito, Ernest; del Puerto-Flores, D.; Doria-Cerezo, A.; Scherpen, Jacquelien M.A.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

This paper studies the optimal power flow problem for resistive DC networks. The gradient method algorithm is written in a port-Hamiltonian form and the stability of the resulting dynamics is studied. Stability conditions are provided for general cyclic networks and a solution, when these conditions

Network integration of distributed power generation

Science.gov (United States)

Dondi, Peter; Bayoumi, Deia; Haederli, Christoph; Julian, Danny; Suter, Marco

The world-wide move to deregulation of the electricity and other energy markets, concerns about the environment, and advances in renewable and high efficiency technologies has led to major emphasis being placed on the use of small power generation units in a variety of forms. The paper reviews the position of distributed generation (DG, as these small units are called in comparison with central power plants) with respect to the installation and interconnection of such units with the classical grid infrastructure. In particular, the status of technical standards both in Europe and USA, possible ways to improve the interconnection situation, and also the need for decisions that provide a satisfactory position for the network operator (who remains responsible for the grid, its operation, maintenance and investment plans) are addressed.

Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

International Nuclear Information System (INIS)

Cheon, Se Woo; Chang Soon Heung; Chung, Hak Yeong

1992-01-01

This paper presents feasibility studies of multiple alarm processing and diagnosis using neural networks. The back-propagation neural network model is applied to the training of multiple alarm patterns for the identification of failure in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. The case studies are performed with emphasis on the applicability of the neural network to pattern recognition problems. It is revealed that the neural network model can identify the cause of multiple alarms properly, even when untrained or sensor-failed alarm symptoms are given. It is also shown that multiple failures are easily identified using the symptoms of multiple alarms

Governmentalizing Gramsci: Topologies of power and passive revolution in Cambodia’s garment production network

OpenAIRE

Arnold, D.; Hess, M.

2017-01-01

This article takes a fresh look at the multiple power relations between state, capital and labor in global production networks. Moving beyond debates about public vs. private governance, it brings together Antonio Gramsci’s concepts of hegemony and the integral state with Michel Foucault’s concepts of governmentality and the “dipositive” in order to analyze the power topologies that permeate global production networks. Using the Cambodian garment production network as example, we scrutinize t...

Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

OpenAIRE

Yang, Shan; Tong, Xiangqian

2016-01-01

Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverte...

A study on the applications of expert systems and neural networks for the development of operator support systems in nuclear power plants

International Nuclear Information System (INIS)

Cheon, Se Woo

1993-02-01

studies on pattern recognition problems in nuclear power plants have been performed. One is transient identification and the other is multiple alarm processing and diagnosis. The backpropagation network (BPN) training algorithm is applied to both of the two studies. The general mapping capability of the neural networks enables to identify a transient or an alarm's fault well. The transient identification is performed by mapping or associating patterns of symptom input vectors to patterns representing transient conditions. In the implemented network, an input layer consists of 24 sensor input nodes, a hidden layer consists of 17 nodes, and an output layer consists of 14-class transient identification nodes. The input values are various parameter trends (increasing/unchanging/decreasing), valve states (open/closed), pump states (operating/stop) and alarm data (on/off). The multiple alarm processing and diagnosis is performed by training multiple alarm patterns for the diagnosis of faults in the RCP system. In the implemented network, an input layer consists of 9-class fault identification nodes. A number of case studies are performed with emphasis on the applicability of the neural networks to the pattern recognition problems. Based on the case studies, it is revealed that the BPN training algorithm can identify the transient or the alarm's fault well, although untrained, incomplete, sensor-failed, or time-varying symptoms are given. Also, multiple transients or alarm's faults are easily identified with a given symptom input vector

Identification of human disease genes from interactome network using graphlet interaction.

Directory of Open Access Journals (Sweden)

Xiao-Dong Wang

Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

Science.gov (United States)

Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

2014-01-01

Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

Analysis and characterization of security regions in power systems. Part I. Load flow feasibility conditions in power networks

Energy Technology Data Exchange (ETDEWEB)

Jarjis, J; Galiana, F D

1980-03-01

A set theoretic analysis of loadflow feasibility of a general power network with arbitrary PQ, PV and slack buses is presented. Load-flow feasibility is that property of a power network defining the theoretical limitations on the bus injections under which a steady state equilibrium exists. The set theoretic analysis is based on the study of the conical loadflow feasibility region. This region is characterised by a set of supporting hyperplanes each of which defines an explicit necessary condition for loadflow feasibility on the bus injections. A quantitative measure of loadflow feasibility for an arbitrary given operating injection vector is defined through a computable scalar stability margin. This stability margin permits the loadflow feasibility of different injections and network structures to be quantitatively compared and analysed.

Power converters for medium voltage networks

CERN Document Server

Islam, Md Rabiul; Zhu, Jianguo

2014-01-01

This book examines a number of topics, mainly in connection with advances in semiconductor devices and magnetic materials and developments in medium and large-scale renewable power plant technologies, grid integration techniques and new converter topologies, including advanced digital control systems for medium-voltage networks. The book's individual chapters provide an extensive compilation of fundamental theories and in-depth information on current research and development trends, while also exploring new approaches to overcoming some critical limitations of conventional grid integration te

Power-Aware Multi-Layer Translucent Network Design: an Integrated OPEX/CAPEX Analysis

DEFF Research Database (Denmark)

Saldaña Cercos, Silvia; Resendo, Leandro C.; Ribeiro, Moisés R. N.

2014-01-01

We propose a three-phase network design model minimizing CAPEX and OPEX in IP-over-WDM architectures. By forbidding reconfiguration (accounting for 58\\% of the OPEX) we achieve only 4.2\\% increase in power consumption at no CAPEX expenses.......We propose a three-phase network design model minimizing CAPEX and OPEX in IP-over-WDM architectures. By forbidding reconfiguration (accounting for 58\\% of the OPEX) we achieve only 4.2\\% increase in power consumption at no CAPEX expenses....

The impact of measurement errors in the identification of regulatory networks

Directory of Open Access Journals (Sweden)

Sato João R

2009-12-01

Full Text Available Abstract Background There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent and non-time series (independent data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models and dependent (autoregressive models data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error. The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Nuclear power plant status diagnostics using artificial neural networks

International Nuclear Information System (INIS)

Bartlett, E.B.; Uhrig, R.E.

1991-01-01

In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results

Impact of distributed generation units with power electronic converters on distribution network protection

NARCIS (Netherlands)

Morren, J.; Haan, de S.W.H.

2008-01-01

An increasing number of distributed generation units (DG units) are connected to the distribution network. These generators affect the operation and coordination of the distribution network protection. The influence from DG units that are coupled to the network with a power electronic converter

Power consumption analysis of constant bit rate video transmission over 3G networks

DEFF Research Database (Denmark)

Ukhanova, Ann; Belyaev, Evgeny; Wang, Le

2012-01-01

This paper presents an analysis of the power consumption of video data transmission with constant bit rate over 3G mobile wireless networks. The work includes the description of the radio resource control transition state machine in 3G networks, followed by a detailed power consumption analysis...... and measurements of the radio link power consumption. Based on this description and analysis, we propose our power consumption model. The power model was evaluated on a smartphone Nokia N900, which follows 3GPP Release 5 and 6 supporting HSDPA/HSUPA data bearers. We also propose a method for parameter selection...... for the 3GPP transition state machine that allows to decrease power consumption on a mobile device taking signaling traffic, buffer size and latency restrictions into account. Furthermore, we discuss the gain in power consumption vs. PSNR for transmitted video and show the possibility of performing power...

Weld defect identification in friction stir welding using power spectral density

Science.gov (United States)

Das, Bipul; Pal, Sukhomay; Bag, Swarup

2018-04-01

Power spectral density estimates are powerful in extraction of useful information retained in signal. In the current research work classical periodogram and Welch periodogram algorithms are used for the estimation of power spectral density for vertical force signal and transverse force signal acquired during friction stir welding process. The estimated spectral densities reveal notable insight in identification of defects in friction stir welded samples. It was observed that higher spectral density against each process signals is a key indication in identifying the presence of possible internal defects in the welded samples. The developed methodology can offer preliminary information regarding presence of internal defects in friction stir welded samples can be best accepted as first level of safeguard in monitoring the friction stir welding process.

Nuclear power plant monitoring using real-time learning neural network

International Nuclear Information System (INIS)

Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

1994-01-01

In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

Directory of Open Access Journals (Sweden)

Xuebin Qin

2014-09-01

Full Text Available In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed chaotic competitive learning network recognizes power cable types. The result shows a good performance using the support vector machine (SVM and improved Particle Swarm Optimization (IPSO-SVM method. The experimental result shows that the fault recognition accuracy reached was 96.2%, using 54 data samples. The network training time is about 0.032 second. The method can achieve cable fault classification effectively.

Power Saving Scheduling Scheme for Internet of Things over LTE/LTE-Advanced Networks

Directory of Open Access Journals (Sweden)

Yen-Wei Kuo

2015-01-01

Full Text Available The devices of Internet of Things (IoT will grow rapidly in the near future, and the power consumption and radio spectrum management will become the most critical issues in the IoT networks. Long Term Evolution (LTE technology will become a promising technology used in IoT networks due to its flat architecture, all-IP network, and greater spectrum efficiency. The 3rd Generation Partnership Project (3GPP specified the Discontinuous Reception (DRX to reduce device’s power consumption. However, the DRX may pose unexpected communication delay due to missing Physical Downlink Control Channel (PDCCH information in sleep mode. Recent studies mainly focus on optimizing DRX parameters to manage the tradeoff between the energy consumption and communication latency. In this paper, we proposed a fuzzy-based power saving scheduling scheme for IoT over the LTE/LTE-Advanced networks to deal with the issues of the radio resource management and power consumption from the scheduling and resource allocation perspective. The proposed scheme considers not only individual IoT device’s real-time requirement but also the overall network performance. The simulation results show that our proposed scheme can meet the requirements of the DRX cycle and scheduling latency and can save about half of energy consumption for IoT devices compared to conventional approaches.

On the Dual-Decomposition-Based Resource and Power Allocation with Sleeping Strategy for Heterogeneous Networks

KAUST Repository

Alsharoa, Ahmad M.; Ghazzai, Hakim; Yaacoub, Elias; Alouini, Mohamed-Slim

2015-01-01

In this paper, the problem of radio and power resource management in long term evolution heterogeneous networks (LTE HetNets) is investigated. The goal is to minimize the total power consumption of the network while satisfying the user quality

Structure Learning in Power Distribution Networks

Energy Technology Data Exchange (ETDEWEB)

Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2015-01-13

Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

International Nuclear Information System (INIS)

Peng Yafu; Hsu, C.-F.

2009-01-01

This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

Network Condition Based Adaptive Control and its Application to Power Balancing in Electrical Grids

DEFF Research Database (Denmark)

Pedersen, Rasmus; Findrik, Mislav; Sloth, Christoffer

2017-01-01

To maintain a reliable and stable power grid there must be balance between consumption and production. To achieve power balance in a system with high penetration of distributed renewable resources and flexible assets, these individual system can be coordinated through a control unit to become part...... of the power balancing effort. Such control strategies require communication networks for exchange of control loop information. In this work, we show how a congested communication network can have a dramatic impact on the control performance of such a power balancing controller. To alleviate potential...

A Review of Frequency Response Analysis Methods for Power Transformer Diagnostics

Directory of Open Access Journals (Sweden)

Saleh Alsuhaibani

2016-10-01

Full Text Available Power transformers play a critical role in electric power networks. Such transformers can suffer failures due to multiple stresses and aging. Thus, assessment of condition and diagnostic techniques are of great importance for improving power network reliability and service continuity. Several techniques are available to diagnose the faults within the power transformer. Frequency response analysis (FRA method is a powerful technique for diagnosing transformer winding deformation and several other types of problems that are caused during manufacture, transportation, installation and/or service life. This paper provides a comprehensive review on FRA methods and their applications in diagnostics and fault identification for power transformers. The paper discusses theory and applications of FRA methods as well as various issues and challenges faced in the application of this method.

Power and delay optimisation in multi-hop wireless networks

KAUST Repository

Xia, Li; Shihada, Basem

2014-01-01

in order to minimise the power consumption and the queueing delay of the whole network. With the assumptions of interference-free links and independently and identically distributed (i.i.d.) channel states, we formulate this problem using a semi

Transportation network development in a liberalized power system, a coordination problem with production

International Nuclear Information System (INIS)

Rious, V.

2007-10-01

This thesis analyses how the long term coordination between generation and transmission is organised in a liberalized power system. It relies on a modular analysis framework that allows us to separate the coordination mechanisms into distinct modules. The governance structure of the power network completes this analysis framework. In a rationale of institutional complementarity, this governance structure impacts the way the TSOs effectively implement the power flow management. The proactive behaviour of the TSO for the coordination of generation and transmission can be explained in two ways. Firstly, the network investment can be the only effective process of long term coordination between generation and transmission. Secondly, when forecasting network investment, the TSO can prepare the accommodation of the fast-built generation units and detect the potential failures of locational incentive signals. (author)

NETWORK CENTRISM OPTIMIZATION OF EXPEDITIOUS SERVICE OF ELEMENTS OF THE POWER SUPPLY SYSTEM

Directory of Open Access Journals (Sweden)

Ye.I. Sokol

2016-06-01

Full Text Available Purpose. Development of precision selection criteria of options of technical realization of effective active and adaptive system of expeditious service of elements of a power supply system in the conditions of network-centric management. Methodology. In development of power supply systems their evolution from the elementary forms using elementary network technologies and models of interactions in power to more irregular shapes within the concept of Smart Grid with elements of network-centric character is observed. This direction is based on Internet-technologies of the last generation, and realize models of power activity which couldn't be realized before. Results. The number of possible options of active and adaptive system of expeditious service of elements of a power supply system is usually rather big and it is difficult to choose the acceptable option by direct search. Elimination of admissible options of the technical realization constructed on the principles of a network centrism means application of the theory of multicriteria optimization from a position of discrete programming. The basis of procedure of elimination is made by algorithm of an assessment of system by criterion of accuracy. Originality. The case of an assessment of the precision characteristic of system at restrictions for the set accuracy is connected with need of decomposition of requirements of all system in general and on separate subsystems. For such decomposition the ratios connecting the accuracy of functioning of a separate subsystem with variations of parameters of all system, and also with precision characteristics of subsystems of the lower levels influencing this subsystem are received. Practical value. In the conditions of the network-centric organization of management of expeditious service of elements of a power supply system elimination of options of subsystems when using precision criterion allows to receive the maximum number of essentially possible

Link Power Budget and Traffict QoS Performance Analysis of Gygabit Passive Optical Network

Science.gov (United States)

Ubaidillah, A.; Alfita, R.; Toyyibah

2018-01-01

Data service of telecommunication network is needed widely in the world; therefore extra wide bandwidth must be provided. For this case, PT. Telekomunikasi Tbk. applies GPON (Gigabit Passive Optical Network) as optical fibre based on telecommunication network system. GPON is a point to a multipoint technology of FTTx (Fiber to The x) that transmits information signals to the subscriber over optical fibre. In GPON trunking system, from OLT (Optical Line Terminal), the network is split to many ONT (Optical Network Terminal) of the subscribers, so it causes path loss and attenuation. In this research, the GPON performance is measured from the link power budget system and the Quality of Service (QoS) of the traffic. And the observation result shows that the link power budget system of this GPON is in good condition. The link power budget values from the mathematical calculation and direct measurement are satisfy the ITU-T G984 Class B standard, that the power level must be between -8 dBm to -27 dBm. While from the traffic performance, the observation result shows that the network resource utility of the subscribers of the observed area is not optimum. The mean of subscriber utility rate is 27.985 bps for upstream and 79.687 bps for downstream. While maximally, It should be 60.800 bps for upstream and 486.400 bps for downstream.

Wind power, network congestion and hydro resource utilisation in the Norwegian power market

International Nuclear Information System (INIS)

Foersund, Finn; Singh, Balbir; Jensen, Trond; Larsen, Cato

2005-01-01

Capacity constraints in electricity networks can have important impacts on utilization of new renewable energy (RE) capacity and incumbent generation resources. Neglect of such impacts in development of RE resources can result in crowding-out of incumbent generation. This trade-off is particularly problematic if the incumbent generation also consists of renewable sources, such as hydropower in the Norwegian electricity system. This paper presents a numerical analysis of the current wind-power development plans in North Norway and their impacts on utilization of hydropower. Policy simulations in paper are conducted using a dynamic partial equilibrium model that is calibrated to reflect the structure of the Nordic power market. The paper draws conclusion and policy implications for integration of RE resources in the Norwegian power market. (Author)

Nuclear power plant fault-diagnosis using artificial neural networks

International Nuclear Information System (INIS)

Kim, Keehoon; Aljundi, T.L.; Bartlett, E.B.

1992-01-01

Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

Wireless coexistence and interference test method for low-power wireless sensor networks

NARCIS (Netherlands)

Serra, R.; Nabi, Majid

2015-01-01

Wireless sensor networks (WSNs) are being increasingly introduced for critical applications such as safety, security and health. One the main characteristic requirements of such networks are that they should function with relative low power. Therefore the wireless links are more vulnerable.

Wireless Power Transfer Protocols in Sensor Networks: Experiments and Simulations

Directory of Open Access Journals (Sweden)

Sotiris Nikoletseas

2017-04-01

Full Text Available Rapid technological advances in the domain of Wireless Power Transfer pave the way for novel methods for power management in systems of wireless devices, and recent research works have already started considering algorithmic solutions for tackling emerging problems. In this paper, we investigate the problem of efficient and balanced Wireless Power Transfer in Wireless Sensor Networks. We employ wireless chargers that replenish the energy of network nodes. We propose two protocols that configure the activity of the chargers. One protocol performs wireless charging focused on the charging efficiency, while the other aims at proper balance of the chargers’ residual energy. We conduct detailed experiments using real devices and we validate the experimental results via larger scale simulations. We observe that, in both the experimental evaluation and the evaluation through detailed simulations, both protocols achieve their main goals. The Charging Oriented protocol achieves good charging efficiency throughout the experiment, while the Energy Balancing protocol achieves a uniform distribution of energy within the chargers.

A Power Balance Aware Wireless Charger Deployment Method for Complete Coverage in Wireless Rechargeable Sensor Networks

Directory of Open Access Journals (Sweden)