Electricity networks: how 'natural' is the monopoly?

International Nuclear Information System (INIS)

Kuenneke, Rolf W.

1999-01-01

This article deals with the changing economic characteristics of the electricity network. Traditionally, electricity networks are considered natural monopolies for various kinds of market failures coincide in this essential part of the electricity infrastructure. Technological induced complementarities between nodes and links are causing network externalities, economies of scale, a high degree of mono-functionality, collective good characteristics and an inherent tendency towards concentrated market structures. It is argued that recent technological trends imply a dramatic change of the network economics, leading to possibilities of inter- and intra-network competition, as well as inter fuel competition. The possible implications for the regulatory framework of this sector are addressed. (Author)

Linear network error correction coding

CERN Document Server

Guang, Xuan

2014-01-01

There are two main approaches in the theory of network error correction coding. In this SpringerBrief, the authors summarize some of the most important contributions following the classic approach, which represents messages by sequences?similar to algebraic coding,?and also briefly discuss the main results following the?other approach,?that uses the theory of rank metric codes for network error correction of representing messages by subspaces. This book starts by establishing the basic linear network error correction (LNEC) model and then characterizes two equivalent descriptions. Distances an

Azerbaijan Technical University’s Experience in Teaching Linear Electrical Circuit Theory

Directory of Open Access Journals (Sweden)

G. A. Mamedov

2006-01-01

Full Text Available An experience in teaching linear electrical circuit theory at the Azerbaijan Technical University is presented in the paper. The paper describes structure of the Linear Electrical Circuit Theory course worked out by the authors that contains a section on electrical calculation of track circuits, information on electro-magnetic compatibility and typical tests for better understanding of the studied subject.

Linear control theory for gene network modeling.

Science.gov (United States)

Shin, Yong-Jun; Bleris, Leonidas

2010-09-16

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Impacts of climate change on electricity network business

International Nuclear Information System (INIS)

Martikainen, A.

2006-04-01

Climate has a significant impact on the electricity network business. The electricity network is under the weather pressure all the time and it is planned and constructed to withstand normal climatic stresses. The electricity network that has been planned and constructed now, is expected to be in operation next 40 years. If climatic stresses change in this period, it can cause significant impacts on electricity network business. If the impacts of climate change are figured out in advance, it is possible to mitigate negative points of climate change and exploit the positive points. In this paper the impact of climate change on electricity network business is presented. The results are based on RCAO climate model scenarios. The climate predictions were composed to the period 2016. 2045. The period 1960.1990 was used as a control period. The climate predictions were composed for precipitation, temperature, hoarfrost, thunder, ground frost and wind. The impacts of the change of the climate variables on electricity network business were estimated from technical and economical points of view. The estimation was based on the change predictions of the climate variables. It is expected that climate change will cause more damages than benefits on the electricity network business. The increase of the number of network faults will be the most significant and demanding disadvantage caused by climate change. If networks are not improved to be more resistant for faults, then thunder, heavy snow and wind cause more damages especially to overhead lines in medium voltage network. Increasing precipitation and decreasing amount of ground frost weaken the strength of soil. The construction work will be more difficult with the present vehicles because wet and unfrozen ground can not carry heavy vehicles. As a consequence of increasing temperature, the demand of heating energy will decrease and the demand of cooling energy will increase. This is significant for the electricity

Linear engine development for series hybrid electric vehicles

Science.gov (United States)

Toth-Nagy, Csaba

This dissertation argues that diminishing oil reserves, concern over global climate change, and desire to improve ambient air quality all demand the development of environment-friendly personal transportation. In certain applications, series hybrid electric vehicles offer an attractive solution to reducing fuel consumption and emissions. Furthermore, linear engines are emerging as a powerplant suited to series HEV applications. In this dissertation, a linear engine/alternator was considered as the auxiliary power unit of a range extender series hybrid electric vehicle. A prototype linear engine/alternator was developed, constructed and tested at West Virginia University. The engine was a 2-stroke, 2-cylinder, dual piston, direct injection, diesel engine. Experiment on the engine was performed to study its behavior. The study variables included mass of the translator, amount of fuel injected, injection timing, load, and stroke with operating frequency and mechanical efficiency as the basis of comparison. The linear engine was analyzed in detail and a simple simulation model was constructed to compare the trends of simulation with the experimental data and to expand on the area where the experimental data were lacking. The simulation was based on a simple and analytical model, rather than a detailed and intensely numerical one. The experimental and theoretical data showed similar trends. Increasing translator mass decreased the operating frequency and increased compression ratio. Larger mass and increased compression ratio improved the ability of the engine to sustain operation and the engine was able to idle on less fuel injected into the cylinder. Increasing the stroke length caused the operating frequency to drop. Increasing fueling or decreasing the load resulted in increased operating frequency. This projects the possibility of using the operating frequency as an input for feedback control of the engine. Injection timing was varied to investigate two different

Linear control theory for gene network modeling.

Directory of Open Access Journals (Sweden)

Yong-Jun Shin

Full Text Available Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain and linear state-space (time domain can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Linear analysis of degree correlations in complex networks

Indian Academy of Sciences (India)

Many real-world networks such as the protein–protein interaction networks and metabolic networks often display nontrivial correlations between degrees of vertices connected by edges. Here, we analyse the statistical methods used usually to describe the degree correlation in the networks, and analytically give linear ...

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....

Forms and Linear Network Codes

DEFF Research Database (Denmark)

Hansen, Johan P.

We present a general theory to obtain linear network codes utilizing forms and obtain explicit families of equidimensional vector spaces, in which any pair of distinct vector spaces intersect in the same small dimension. The theory is inspired by the methods of the author utilizing the osculating...... spaces of Veronese varieties. Linear network coding transmits information in terms of a basis of a vector space and the information is received as a basis of a possibly altered vector space. Ralf Koetter and Frank R. Kschischang introduced a metric on the set af vector spaces and showed that a minimal...... distance decoder for this metric achieves correct decoding if the dimension of the intersection of the transmitted and received vector space is sufficiently large. The vector spaces in our construction are equidistant in the above metric and the distance between any pair of vector spaces is large making...

Permitted and forbidden sets in symmetric threshold-linear networks.

Science.gov (United States)

Hahnloser, Richard H R; Seung, H Sebastian; Slotine, Jean-Jacques

2003-03-01

The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, convergence to attractive fixed points, and multistability, all fundamental aspects of cortical information processing. However, these conditions were only sufficient, and it remained unclear which were the minimal (necessary) conditions for convergence and multistability. We show that symmetric threshold-linear networks converge to a set of attractive fixed points if and only if the network matrix is copositive. Furthermore, the set of attractive fixed points is nonconnected (the network is multiattractive) if and only if the network matrix is not positive semidefinite. There are permitted sets of neurons that can be coactive at a stable steady state and forbidden sets that cannot. Permitted sets are clustered in the sense that subsets of permitted sets are permitted and supersets of forbidden sets are forbidden. By viewing permitted sets as memories stored in the synaptic connections, we provide a formulation of long-term memory that is more general than the traditional perspective of fixed-point attractor networks. There is a close correspondence between threshold-linear networks and networks defined by the generalized Lotka-Volterra equations.

Osculating Spaces of Varieties and Linear Network Codes

DEFF Research Database (Denmark)

Hansen, Johan P.

2013-01-01

We present a general theory to obtain good linear network codes utilizing the osculating nature of algebraic varieties. In particular, we obtain from the osculating spaces of Veronese varieties explicit families of equidimensional vector spaces, in which any pair of distinct vector spaces...... intersects in the same dimension. Linear network coding transmits information in terms of a basis of a vector space and the information is received as a basis of a possible altered vector space. Ralf Koetter and Frank R. Kschischang introduced a metric on the set af vector spaces and showed that a minimal...... distance decoder for this metric achieves correct decoding if the dimension of the intersection of the transmitted and received vector space is sufficiently large. The obtained osculating spaces of Veronese varieties are equidistant in the above metric. The parameters of the resulting linear network codes...

Osculating Spaces of Varieties and Linear Network Codes

DEFF Research Database (Denmark)

Hansen, Johan P.

We present a general theory to obtain good linear network codes utilizing the osculating nature of algebraic varieties. In particular, we obtain from the osculating spaces of Veronese varieties explicit families of equideminsional vector spaces, in which any pair of distinct vector spaces...... intersects in the same dimension. Linear network coding transmits information in terms of a basis of a vector space and the information is received as a basis of a possible altered vector space. Ralf Koetter and Frank R. Kschischang introduced a metric on the set af vector spaces and showed that a minimal...... distance decoder for this metric achieves correct decoding if the dimension of the intersection of the transmitted and received vector space is sufficiently large. The obtained osculating spaces of Veronese varieties are equidistant in the above metric. The parameters of the resulting linear network codes...

Combined natural gas and electricity network pricing

Energy Technology Data Exchange (ETDEWEB)

Morais, M.S.; Marangon Lima, J.W. [Universidade Federal de Itajuba, Rua Dr. Daniel de Carvalho, no. 296, Passa Quatro, Minas Gerais, CEP 37460-000 (Brazil)

2007-04-15

The introduction of competition to electricity generation and commercialization has been the main focus of many restructuring experiences around the world. The open access to the transmission network and a fair regulated tariff have been the keystones for the development of the electricity market. Parallel to the electricity industry, the natural gas business has great interaction with the electricity market in terms of fuel consumption and energy conversion. Given that the transmission and distribution monopolistic activities are very similar to the natural gas transportation through pipelines, economic regulation related to the natural gas network should be coherent with the transmission counterpart. This paper shows the application of the main wheeling charge methods, such as MW/gas-mile, invested related asset cost (IRAC) and Aumman-Shapley allocation, to both transmission and gas network. Stead-state equations are developed to adequate the various pricing methods. Some examples clarify the results, in terms of investments for thermal generation plants and end consumers, when combined pricing methods are used for transmission and gas networks. The paper also shows that the synergies between gas and electricity industry should be adequately considered, otherwise wrong economic signals are sent to the market players. (author)

Impacts of climate change on electricity network business

International Nuclear Information System (INIS)

Auvinen, O.; Martikainen, A.

2006-01-01

In this project the impact of climate change on electricity network business was study. The results are based on RCAO climate model scenarios. The climate predictions were composed to the period 2016- 2045. The period 1960-1990 was used as a control period. The climate predictions were composed for precipitation, temperature, hoarfrost, thunder, ground frost and wind. Impacts of the change of the climate variables on electricity network business were estimated from technical and economical points of view. It is expected that climate change will cause more damages than benefits on the electricity network business. The increase of the number of network faults will be the most significant and demanding disadvantage caused by climate change in distribution network. If networks are not improved to be more resistant for faults, then thunder, heavy snow and wind cause more damages especially to overhead lines in medium voltage network. Increasing precipitation and decreasing amount of ground frost weaken the strength of soil. The construction work will be more difficult with the present vehicles because wet and unfrozen ground can not carry heavy vehicles. As a consequence of increasing temperature, the demand of heating energy will decrease and the demand of cooling energy will increase. This is significant for the electricity consumption and the peak load of temperature-dependent electricity users. (orig.)

Implementation of neural network based non-linear predictive

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1998-01-01

The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

Australia's long-term electricity demand forecasting using deep neural networks

OpenAIRE

Hamedmoghadam, Homayoun; Joorabloo, Nima; Jalili, Mahdi

2018-01-01

Accurate prediction of long-term electricity demand has a significant role in demand side management and electricity network planning and operation. Demand over-estimation results in over-investment in network assets, driving up the electricity prices, while demand under-estimation may lead to under-investment resulting in unreliable and insecure electricity. In this manuscript, we apply deep neural networks to predict Australia's long-term electricity demand. A stacked autoencoder is used in...

A neural network image reconstruction technique for electrical impedance tomography

International Nuclear Information System (INIS)

Adler, A.; Guardo, R.

1994-01-01

Reconstruction of Images in Electrical Impedance Tomography requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. This paper presents a reconstruction algorithm using neural network techniques which calculates a linear approximation of the inverse problem directly from finite element simulations of the forward problem. This inverse is adapted to the geometry of the medium and the signal-to-noise ratio (SNR) used during network training. Results show good conductivity reconstruction where measurement SNR is similar to the training conditions. The advantages of this method are its conceptual simplicity and ease of implementation, and the ability to control the compromise between the noise performance and resolution of the image reconstruction

Noticing climate change in electricity network design and construction

International Nuclear Information System (INIS)

Syri, S.; Martikeinen, A.; Lehtonen, M.

2007-01-01

The climate change is widely known to cause remarkable effects to electricity network systems on the whole. Some of the changes are good but the most of the changes cause disadvantages to electricity network. Consequence of climate change, blackouts can be long-standing which affect remarkable society and economic life. Most of electricity networks are coming to a renovation phase and the solutions, that are being made nowadays, affect still after decades. Taking account of climate change, now when networks are being developed and planned, it is possible to avoid possible large repair operation and increase reliability of distribution in the future. The aim of this project is to clarify how climate change should be noticed in planning and construction processes. According to the results of this project electricity network companies can be prepared for climate change by developing planning processes and network cost effectively. Also construction processes are being developed but emphasis is on planning process. The results and developed knowledge of VTT research project 'Impacts of climate change on electricity network business' are exploited in this project. In addition, impacts of climate change on cables and transformers are analyzed in collaboration with TKK in the project. (orig.)

modeling and optimization of an electric power distribution network

African Journals Online (AJOL)

user

EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...

Linear approximation model network and its formation via ...

Indian Academy of Sciences (India)

niques, an alternative `linear approximation model' (LAM) network approach is .... network is LPV, existing LTI theory is difficult to apply (Kailath 1980). ..... Beck J V, Arnold K J 1977 Parameter estimation in engineering and science (New York: ...

Combined effect of chemical and electrical synapses in Hindmarsh-Rose neural networks on synchronization and the rate of information.

Science.gov (United States)

Baptista, M S; Moukam Kakmeni, F M; Grebogi, C

2010-09-01

In this work we studied the combined action of chemical and electrical synapses in small networks of Hindmarsh-Rose (HR) neurons on the synchronous behavior and on the rate of information produced (per time unit) by the networks. We show that if the chemical synapse is excitatory, the larger the chemical synapse strength used the smaller the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find desynchronous behavior. Otherwise, if the chemical synapse is inhibitory, the larger the chemical synapse strength used the larger the electrical synapse strength needed to achieve complete synchronization, and for moderate synaptic strengths one should expect to find synchronous behaviors. Finally, we show how to calculate semianalytically an upper bound for the rate of information produced per time unit (Kolmogorov-Sinai entropy) in larger networks. As an application, we show that this upper bound is linearly proportional to the number of neurons in a network whose neurons are highly connected.

EMMNet: Sensor Networking for Electricity Meter Monitoring

Directory of Open Access Journals (Sweden)

Zhi-Ting Lin

2010-06-01

Full Text Available Smart sensors are emerging as a promising technology for a large number of application domains. This paper presents a collection of requirements and guidelines that serve as a basis for a general smart sensor architecture to monitor electricity meters. It also presents an electricity meter monitoring network, named EMMNet, comprised of data collectors, data concentrators, hand-held devices, a centralized server, and clients. EMMNet provides long-distance communication capabilities, which make it suitable suitable for complex urban environments. In addition, the operational cost of EMMNet is low, compared with other existing remote meter monitoring systems based on GPRS. A new dynamic tree protocol based on the application requirements which can significantly improve the reliability of the network is also proposed. We are currently conducting tests on five networks and investigating network problems for further improvements. Evaluation results indicate that EMMNet enhances the efficiency and accuracy in the reading, recording, and calibration of electricity meters.

EMMNet: sensor networking for electricity meter monitoring.

Science.gov (United States)

Lin, Zhi-Ting; Zheng, Jie; Ji, Yu-Sheng; Zhao, Bao-Hua; Qu, Yu-Gui; Huang, Xu-Dong; Jiang, Xiu-Fang

2010-01-01

Smart sensors are emerging as a promising technology for a large number of application domains. This paper presents a collection of requirements and guidelines that serve as a basis for a general smart sensor architecture to monitor electricity meters. It also presents an electricity meter monitoring network, named EMMNet, comprised of data collectors, data concentrators, hand-held devices, a centralized server, and clients. EMMNet provides long-distance communication capabilities, which make it suitable suitable for complex urban environments. In addition, the operational cost of EMMNet is low, compared with other existing remote meter monitoring systems based on GPRS. A new dynamic tree protocol based on the application requirements which can significantly improve the reliability of the network is also proposed. We are currently conducting tests on five networks and investigating network problems for further improvements. Evaluation results indicate that EMMNet enhances the efficiency and accuracy in the reading, recording, and calibration of electricity meters.

Integration of electric drive vehicles in the Danish electricity network with high wind power penetration

DEFF Research Database (Denmark)

Chandrashekhara, Divya K; Østergaard, Jacob; Larsen, Esben

2010-01-01

/conventional) which are likely to fuel these cars. The study was carried out considering the Danish electricity network state around 2025, when the EDV penetration levels would be significant enough to have an impact on the power system. Some of the interesting findings of this study are - EDV have the potential......This paper presents the results of a study carried out to examine the feasibility of integrating electric drive vehicles (EDV) in the Danish electricity network which is characterised by high wind power penetration. One of the main aims of this study was to examine the effect of electric drive...... vehicles on the Danish electricity network, wind power penetration and electricity market. In particular the study examined the effect of electric drive vehicles on the generation capacity constraints, load curve, cross border transmission capacity and the type of generating sources (renewable...

A parallel algorithm for solving linear equations arising from one-dimensional network problems

International Nuclear Information System (INIS)

Mesina, G.L.

1991-01-01

One-dimensional (1-D) network problems, such as those arising from 1- D fluid simulations and electrical circuitry, produce systems of sparse linear equations which are nearly tridiagonal and contain a few non-zero entries outside the tridiagonal. Most direct solution techniques for such problems either do not take advantage of the special structure of the matrix or do not fully utilize parallel computer architectures. We describe a new parallel direct linear equation solution algorithm, called TRBR, which is especially designed to take advantage of this structure on MIMD shared memory machines. The new method belongs to a family of methods which split the coefficient matrix into the sum of a tridiagonal matrix T and a matrix comprised of the remaining coefficients R. Efficient tridiagonal methods are used to algebraically simplify the linear system. A smaller auxiliary subsystem is created and solved and its solution is used to calculate the solution of the original system. The newly devised BR method solves the subsystem. The serial and parallel operation counts are given for the new method and related earlier methods. TRBR is shown to have the smallest operation count in this class of direct methods. Numerical results are given. Although the algorithm is designed for one-dimensional networks, it has been applied successfully to three-dimensional problems as well. 20 refs., 2 figs., 4 tabs

On the dynamic analysis of piecewise-linear networks

NARCIS (Netherlands)

Heemels, WPMH; Camlibel, MK; Schumacher, JM

Piecewise-linear (PL) modeling is often used to approximate the behavior of nonlinear circuits. One of the possible PL modeling methodologies is based on the linear complementarity problem, and this approach has already been used extensively in the circuits and systems community for static networks.

Small diameter symmetric networks from linear groups

Science.gov (United States)

Campbell, Lowell; Carlsson, Gunnar E.; Dinneen, Michael J.; Faber, Vance; Fellows, Michael R.; Langston, Michael A.; Moore, James W.; Multihaupt, Andrew P.; Sexton, Harlan B.

1992-01-01

In this note is reported a collection of constructions of symmetric networks that provide the largest known values for the number of nodes that can be placed in a network of a given degree and diameter. Some of the constructions are in the range of current potential engineering significance. The constructions are Cayley graphs of linear groups obtained by experimental computation.

Sensitivity analysis of linear programming problem through a recurrent neural network

Science.gov (United States)

Das, Raja

2017-11-01

In this paper we study the recurrent neural network for solving linear programming problems. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate the sensitivity analysis of linear programming problem through the neural network. A detailed example is also presented to demonstrate the performance of the recurrent neural network.

Accelerated reliability testing of highly aligned single-walled carbon nanotube networks subjected to DC electrical stressing.

Science.gov (United States)

Strus, Mark C; Chiaramonti, Ann N; Kim, Young Lae; Jung, Yung Joon; Keller, Robert R

2011-07-01

We investigate the electrical reliability of nanoscale lines of highly aligned, networked, metallic/semiconducting single-walled carbon nanotubes (SWCNTs) fabricated through a template-based fluidic assembly process. We find that these SWCNT networks can withstand DC current densities larger than 10 MA cm(-2) for several hours and, in some cases, several days. We develop test methods that show that the degradation rate, failure predictability and total device lifetime can be linked to the initial resistance. Scanning electron and transmission electron microscopy suggest that fabrication variability plays a critical role in the rate of degradation, and we offer an empirical method of quickly determining the long-term performance of a network. We find that well-fabricated lines subject to constant electrical stress show a linear accumulation of damage reminiscent of electromigration in metallic interconnects, and we explore the underlying physical mechanisms that could cause such behavior.

Implementation of neural network based non-linear predictive control

DEFF Research Database (Denmark)

Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

1999-01-01

This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...... on an efficient quasi-Newton algorithm. The performance is demonstrated on a pneumatic servo system....

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Directory of Open Access Journals (Sweden)

Asmau M. Ahmed

2017-07-01

Full Text Available Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1 The mixing process should occur at macroscopic level and (2 Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model. Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs

Directory of Open Access Journals (Sweden)

Jaime Buitrago

2017-01-01

Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.

STRATEGIC RESEARCH AGENDA FOR EUROPE’S ELECTRICITY NETWORKS OF THE FUTURE

DEFF Research Database (Denmark)

Bamberger, Yves; Baptista, João; Botting, Duncan

The first milestone towards the establishment of a common strategy for the development of Europe’s electricity networks was set in April 2006 when the paper ‘Vision and Strategy for Europe’s Electricity Networks of the Future’1 was published. In this Vision, future electricity markets and networks...

Incorporating network effects in a competitive electricity industry. An Australian perspective

International Nuclear Information System (INIS)

Outhred, H.; Kaye, J.

1996-01-01

The role of an electricity network in a competitive electricity industry is reviewed, the nation's experience with transmission pricing is discussed, and a 'Nodal Auction Model' for incorporating network effects in a competitive electricity industry is proposed. The model uses a computer-based auction procedure to address both the spatial issues associated with an electricity network and the temporal issues associated with operation scheduling. The objective is to provide a market framework that addresses both network effects and operation scheduling in a coordinated implementation of spot pricing theory. 12 refs

Network governance in electricity distribution: Public utility or commodity

International Nuclear Information System (INIS)

Kuenneke, Rolf; Fens, Theo

2005-01-01

This paper addresses the question whether the operation and management of electricity distribution networks in a liberalized market environment evolves into a market driven commodity business or might be perceived as a genuine public utility task. A framework is developed to classify and compare different institutional arrangements according to the public utility model and the commodity model. These models are exemplified for the case of the Dutch electricity sector. It appears that the institutional organization of electricity distribution networks is at the crossroads of two very different institutional development paths. They develop towards commercial business if the system characteristics of the electricity sector remain basically unchanged to the traditional situation. If however innovative technological developments allow for a decentralization and decomposition of the electricity system, distribution networks might be operated as public utilities while other energy services are exploited commercially. (Author)

Filamentation and networking of electric currents in dense Z-pinch plasmas

International Nuclear Information System (INIS)

Kukushkin, A.B.; Rantsev-Kartinov, V.A.

2001-01-01

The results of high-resolution processing using the multilevel dynamical contrasting method of earlier experiments on linear Z-pinches are presented which illustrate formation of a dynamical percolating network woven by long-living filaments of electric current. A qualitative approach is outlined which treats long-living filaments as a classical plasma formation governed by the long-range quantum bonds provided, at the microscopical level, by nanotubes of elements of optimal valence. The self-similarity of structuring in laboratory and cosmic plasmas is shown, and examples are found of nanotube-like and/or fullerene-like structures of cosmic length scales. (author)

Filamentation and networking of electric currents in dense Z-pinch plasmas

International Nuclear Information System (INIS)

Kukushkin, A.B.; Rantsev-Kartinov, V.A.

1999-01-01

The results of high-resolution processing using the multilevel dynamical contrasting method of earlier experiments on linear Z-pinches are presented which illustrate formation of a dynamical percolating network woven by long-living filaments of electric current. A qualitative approach is outlined which treats long-living filaments as a classical plasma formation governed by the long-range quantum bonds provided, at the micro-scopical level, by nanotubes of elements of optimal valence. The self-similarity of structuring in laboratory and cosmic plasmas is shown, and examples are found of nanotube-like and/or fullerene-like structures of cosmic length scales. (author)

Decomposed Implicit Models of Piecewise - Linear Networks

Directory of Open Access Journals (Sweden)

J. Brzobohaty

1992-05-01

Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.

Predicting musically induced emotions from physiological inputs: linear and neural network models.

Science.gov (United States)

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

A novel recurrent neural network with finite-time convergence for linear programming.

Science.gov (United States)

Liu, Qingshan; Cao, Jinde; Chen, Guanrong

2010-11-01

In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.

A family of quantization based piecewise linear filter networks

DEFF Research Database (Denmark)

Sørensen, John Aasted

1992-01-01

A family of quantization-based piecewise linear filter networks is proposed. For stationary signals, a filter network from this family is a generalization of the classical Wiener filter with an input signal and a desired response. The construction of the filter network is based on quantization...... of the input signal x(n) into quantization classes. With each quantization class is associated a linear filter. The filtering at time n is carried out by the filter belonging to the actual quantization class of x(n ) and the filters belonging to the neighbor quantization classes of x(n) (regularization......). This construction leads to a three-layer filter network. The first layer consists of the quantization class filters for the input signal. The second layer carries out the regularization between neighbor quantization classes, and the third layer constitutes a decision of quantization class from where the resulting...

Graphical reduction of reaction networks by linear elimination of species

DEFF Research Database (Denmark)

Saez Cornellana, Meritxell; Wiuf, Carsten; Feliu, Elisenda

2017-01-01

We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties...

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

Market tools: the immaterial part of the electricity transmission network

International Nuclear Information System (INIS)

Maillard, Dominique

2014-01-01

The author first evokes the activities of RTE (Reseau de transport d'electricite - the French power transmission network) to improve the performance of its technical and industrial equipment (notably equipment evolution, maintenance policies) with, for example, the installation of a fibre optic network for network control automation, the development of software for a better exploitation and steering of electricity fluxes, notably the electricity produced by wind and photovoltaic power. He more particularly addresses the role of RTE in the construction of the electricity market. He outlines the role of the European electricity market in the economic optimization, the new approaches and tools for a higher flexibility of the electric system, the expertise of RTE, and the perspective of always more smart grids

Marginal cost of electricity conservation: an application of linear program

International Nuclear Information System (INIS)

Silveira, A.M. da; Hollanda, J.B. de

1987-01-01

This paper is addressed ti the planning of electricity industry when the use of energetically efficient appliances (conservation) is financed by the utilities. It is based on the Linear Programming Model proposed by Masse and Boiteaux for planning of conventional energy sources, where one unity of electricity (Kw/Kw h) saved is treated as if it were a generator of equivalent size. In spite of the formal simplicity of the models it can support interesting concessions on the subject of a electrical energy conservation policy. (author)

Will electrical cyber-physical interdependent networks undergo first-order transition under random attacks?

Science.gov (United States)

Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting

2016-10-01

Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.

Developing electricity distribution networks and their regulation to support sustainable energy

International Nuclear Information System (INIS)

Shaw, Rita; Attree, Mike; Jackson, Tim

2010-01-01

A more sustainable energy system will alter the current patterns of electricity demand and generation. This means technical, commercial and regulatory change for electricity network systems such as distribution networks. This paper traces the links in Great Britain between changes in energy policy since privatisation, changes in the objectives of the electricity regulator and changes in the objectives of the distribution networks and their owners, the distribution network operators (DNOs). The paper identifies tensions in regulatory policy and suggests reforms to the regulatory framework to support a lower-carbon future. DNOs are licensed regional infrastructure providers. In addition to their network services, the network companies can potentially deliver public policy objectives to facilitate heat infrastructure, energy-efficiency and distributed renewables. The paper identifies the potential benefits of a novel approach to facilitating renewable energy feed-in tariffs for electricity and heat, using DNOs.

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.

Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding

OpenAIRE

Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Fitzek, Frank

2014-01-01

This work proposes a new protocol applying on–the–fly random linear network coding in wireless mesh net-works. The protocol provides increased reliability, low delay,and high throughput to the upper layers, while being obliviousto their specific requirements. This seemingly conflicting goalsare achieved by design, using an on–the–fly network codingstrategy. Our protocol also exploits relay nodes to increasethe overall performance of individual links. Since our protocolnaturally masks random p...

A new chaotic Hopfield network with piecewise linear activation function

International Nuclear Information System (INIS)

Peng-Sheng, Zheng; Wan-Sheng, Tang; Jian-Xiong, Zhang

2010-01-01

This paper presents a new chaotic Hopfield network with a piecewise linear activation function. The dynamic of the network is studied by virtue of the bifurcation diagram, Lyapunov exponents spectrum and power spectrum. Numerical simulations show that the network displays chaotic behaviours for some well selected parameters

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

Non-Linear State Estimation Using Pre-Trained Neural Networks

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2010-01-01

effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve...... other parametric methods around regions with strong non-linearities by including them inside the network....

Neural network based photovoltaic electrical forecasting in south Algeria

International Nuclear Information System (INIS)

Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

2014-01-01

Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

Developing electricity distribution networks and their regulation to support sustainable energy

Energy Technology Data Exchange (ETDEWEB)

Shaw, Rita; Attree, Mike [Electricity North West Ltd., 304 Bridgewater Place, Birchwood, Warrington, Cheshire WA3 6XG (United Kingdom); Jackson, Tim [RESOLVE, Centre for Environmental Strategy D3, University of Surrey, Guildford, Surrey GU2 7XH (United Kingdom)

2010-10-15

A more sustainable energy system will alter the current patterns of electricity demand and generation. This means technical, commercial and regulatory change for electricity network systems such as distribution networks. This paper traces the links in Great Britain between changes in energy policy since privatisation, changes in the objectives of the electricity regulator and changes in the objectives of the distribution networks and their owners, the distribution network operators (DNOs). The paper identifies tensions in regulatory policy and suggests reforms to the regulatory framework to support a lower-carbon future. DNOs are licensed regional infrastructure providers. In addition to their network services, the network companies can potentially deliver public policy objectives to facilitate heat infrastructure, energy-efficiency and distributed renewables. The paper identifies the potential benefits of a novel approach to facilitating renewable energy feed-in tariffs for electricity and heat, using DNOs. (author)

Automated electric valve for electrokinetic separation in a networked microfluidic chip.

Science.gov (United States)

Cui, Huanchun; Huang, Zheng; Dutta, Prashanta; Ivory, Cornelius F

2007-02-15

This paper describes an automated electric valve system designed to reduce dispersion and sample loss into a side channel when an electrokinetically mobilized concentration zone passes a T-junction in a networked microfluidic chip. One way to reduce dispersion is to control current streamlines since charged species are driven along them in the absence of electroosmotic flow. Computer simulations demonstrate that dispersion and sample loss can be reduced by applying a constant additional electric field in the side channel to straighten current streamlines in linear electrokinetic flow (zone electrophoresis). This additional electric field was provided by a pair of platinum microelectrodes integrated into the chip in the vicinity of the T-junction. Both simulations and experiments of this electric valve with constant valve voltages were shown to provide unsatisfactory valve performance during nonlinear electrophoresis (isotachophoresis). On the basis of these results, however, an automated electric valve system was developed with improved valve performance. Experiments conducted with this system showed decreased dispersion and increased reproducibility as protein zones isotachophoretically passed the T-junction. Simulations of the automated electric valve offer further support that the desired shape of current streamlines was maintained at the T-junction during isotachophoresis. Valve performance was evaluated at different valve currents based on statistical variance due to dispersion. With the automated control system, two integrated microelectrodes provide an effective way to manipulate current streamlines, thus acting as an electric valve for charged species in electrokinetic separations.

Equidistant Linear Network Codes with maximal Error-protection from Veronese Varieties

DEFF Research Database (Denmark)

Hansen, Johan P.

2012-01-01

Linear network coding transmits information in terms of a basis of a vector space and the information is received as a basis of a possible altered vectorspace. Ralf Koetter and Frank R. Kschischang in Coding for errors and erasures in random network coding (IEEE Transactions on Information Theory...... construct explicit families of vector-spaces of constant dimension where any pair of distinct vector-spaces are equidistant in the above metric. The parameters of the resulting linear network codes which have maximal error-protection are determined....

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

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Science.gov (United States)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Optimal traffic control in highway transportation networks using linear programming

KAUST Repository

Li, Yanning

2014-06-01

This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.

Design of pressure-driven microfluidic networks using electric circuit analogy.

Science.gov (United States)

Oh, Kwang W; Lee, Kangsun; Ahn, Byungwook; Furlani, Edward P

2012-02-07

This article reviews the application of electric circuit methods for the analysis of pressure-driven microfluidic networks with an emphasis on concentration- and flow-dependent systems. The application of circuit methods to microfluidics is based on the analogous behaviour of hydraulic and electric circuits with correlations of pressure to voltage, volumetric flow rate to current, and hydraulic to electric resistance. Circuit analysis enables rapid predictions of pressure-driven laminar flow in microchannels and is very useful for designing complex microfluidic networks in advance of fabrication. This article provides a comprehensive overview of the physics of pressure-driven laminar flow, the formal analogy between electric and hydraulic circuits, applications of circuit theory to microfluidic network-based devices, recent development and applications of concentration- and flow-dependent microfluidic networks, and promising future applications. The lab-on-a-chip (LOC) and microfluidics community will gain insightful ideas and practical design strategies for developing unique microfluidic network-based devices to address a broad range of biological, chemical, pharmaceutical, and other scientific and technical challenges.

The linear electric motor: Instability at 1,000 g's

International Nuclear Information System (INIS)

Hunter, S.

1997-01-01

When fluid of high density is supported against gravity by a less dense liquid, the system is unstable, and microscopic perturbations grow at the interface between the fluids. This phenomenon, called the Rayleigh-Taylor instability, also occurs when a bottle of oil-and-vinegar salad dressing is turned upside down. The instability causes spikes of the dense fluid to penetrate the light fluid, while bubbles of the lighter fluid rise into the dense fluid. The same phenomenon occurs when a light fluid is used to accelerate a dense fluid, causing the two fluids to mix at a very high rate. For example, during the implosion of an ICF capsule, this instability can cause enough mixing to contaminate, cool, and degrade the yield of the thermonuclear fuel. The LEM is an excellent tool for studying this instability, but what is it? Think of a miniature high-speed electric train (the container) hurtling down a track (the electrodes) while diagnostic equipment (optical and laser) photographs it. The LEM, consists of four linear electrodes, or rails, that carry an electrical current to a pair of sliding armatures on the container. A magnetic field is produced that works in concert with the rail-armature current to accelerate the container--just as in an electric motor, but in a linear fashion rather than in rotation. The magnetic field is augmented with elongated coils just as in a conventional electric motor. This configuration also helps hold the armatures against the electrodes to prevent arcing. The electrical energy (0.6 megajoules) is provided by 16 capacitor banks that can be triggered independently to produce different acceleration profiles (i.e., how the acceleration varies with time)

Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

Directory of Open Access Journals (Sweden)

Karin Kandananond

2011-08-01

Full Text Available Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA, artificial neural network (ANN and multiple linear regression (MLR—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance

Transiently chaotic neural networks with piecewise linear output functions

Energy Technology Data Exchange (ETDEWEB)

Chen, S.-S. [Department of Mathematics, National Taiwan Normal University, Taipei, Taiwan (China); Shih, C.-W. [Department of Applied Mathematics, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu, Taiwan (China)], E-mail: cwshih@math.nctu.edu.tw

2009-01-30

Admitting both transient chaotic phase and convergent phase, the transiently chaotic neural network (TCNN) provides superior performance than the classical networks in solving combinatorial optimization problems. We derive concrete parameter conditions for these two essential dynamic phases of the TCNN with piecewise linear output function. The confirmation for chaotic dynamics of the system results from a successful application of the Marotto theorem which was recently clarified. Numerical simulation on applying the TCNN with piecewise linear output function is carried out to find the optimal solution of a travelling salesman problem. It is demonstrated that the performance is even better than the previous TCNN model with logistic output function.

Non-linear feedback neural networks VLSI implementations and applications

CERN Document Server

Ansari, Mohd Samar

2014-01-01

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

Directory of Open Access Journals (Sweden)

Weide Li

2017-01-01

Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

Relative localization in wireless sensor networks for measurement of electric fields under HVDC transmission lines.

Science.gov (United States)

Cui, Yong; Wang, Qiusheng; Yuan, Haiwen; Song, Xiao; Hu, Xuemin; Zhao, Luxing

2015-02-04

In the wireless sensor networks (WSNs) for electric field measurement system under the High-Voltage Direct Current (HVDC) transmission lines, it is necessary to obtain the electric field distribution with multiple sensors. The location information of each sensor is essential to the correct analysis of measurement results. Compared with the existing approach which gathers the location information by manually labelling sensors during deployment, the automatic localization can reduce the workload and improve the measurement efficiency. A novel and practical range-free localization algorithm for the localization of one-dimensional linear topology wireless networks in the electric field measurement system is presented. The algorithm utilizes unknown nodes' neighbor lists based on the Received Signal Strength Indicator (RSSI) values to determine the relative locations of nodes. The algorithm is able to handle the exceptional situation of the output permutation which can effectively improve the accuracy of localization. The performance of this algorithm under real circumstances has been evaluated through several experiments with different numbers of nodes and different node deployments in the China State Grid HVDC test base. Results show that the proposed algorithm achieves an accuracy of over 96% under different conditions.

Relative Localization in Wireless Sensor Networks for Measurement of Electric Fields under HVDC Transmission Lines

Directory of Open Access Journals (Sweden)

Yong Cui

2015-02-01

Full Text Available In the wireless sensor networks (WSNs for electric field measurement system under the High-Voltage Direct Current (HVDC transmission lines, it is necessary to obtain the electric field distribution with multiple sensors. The location information of each sensor is essential to the correct analysis of measurement results. Compared with the existing approach which gathers the location information by manually labelling sensors during deployment, the automatic localization can reduce the workload and improve the measurement efficiency. A novel and practical range-free localization algorithm for the localization of one-dimensional linear topology wireless networks in the electric field measurement system is presented. The algorithm utilizes unknown nodes’ neighbor lists based on the Received Signal Strength Indicator (RSSI values to determine the relative locations of nodes. The algorithm is able to handle the exceptional situation of the output permutation which can effectively improve the accuracy of localization. The performance of this algorithm under real circumstances has been evaluated through several experiments with different numbers of nodes and different node deployments in the China State Grid HVDC test base. Results show that the proposed algorithm achieves an accuracy of over 96% under different conditions.

AN APPLICATION FOR EFFICIENT TELECOMMUNICATION NETWORKS PROVISIONING USING LINEAR PROGRAMMING

Directory of Open Access Journals (Sweden)

Maria Augusta Soares Machado

2015-03-01

Full Text Available This paper presents a practical proposition for the application of the Linear Programming quantitative method in order to assist planning and control of customer circuit delivery activities in telecommunications companies working with the corporative market. Based upon data provided for by a telecom company operating in Brazil, the Linear Programming method was employed for one of the classical problems of determining the optimum mix of production quantities for a set of five products of that company: Private Telephone Network, Internet Network, Intranet Network, Low Speed Data Network, and High Speed Data Network, in face of several limitations of the productive resources, seeking to maximize the company’s monthly revenue. By fitting the production data available into a primary model, observation was made as to what number of monthly activations for each product would be mostly optimized in order to achieve maximum revenues in the company. The final delivery of a complete network was not observed but the delivery of the circuits that make it up, and this was a limiting factor for the study herein, which, however, brings an innovative proposition for the planning of private telecommunications network provisioning.

Electric Vehicle Integration into Modern Power Networks

DEFF Research Database (Denmark)

software tools to assess the impacts resulting from the electric vehicles deployment on the steady state and dynamic operation of electricity grids, identifies strategies to mitigate them and the possibility to support simultaneously large-scale integration of renewable energy sources. New business models......Electric Vehicle Integration into Modern Power Networks provides coverage of the challenges and opportunities posed by the progressive integration of electric drive vehicles. Starting with a thorough overview of the current electric vehicle and battery state-of-the-art, this work describes dynamic...... and control management architectures, as well as the communication infrastructure required to integrate electric vehicles as active demand are presented. Finally, regulatory issues of integrating electric vehicles into modern power systems are addressed. Inspired by two courses held under the EES...

Electric Vehicle Integration into Modern Power Networks

DEFF Research Database (Denmark)

Electric Vehicle Integration into Modern Power Networks provides coverage of the challenges and opportunities posed by the progressive integration of electric drive vehicles. Starting with a thorough overview of the current electric vehicle and battery state-of-the-art, this work describes dynamic...... software tools to assess the impacts resulting from the electric vehicles deployment on the steady state and dynamic operation of electricity grids, identifies strategies to mitigate them and the possibility to support simultaneously large-scale integration of renewable energy sources. New business models...... and control management architectures, as well as the communication infrastructure required to integrate electric vehicles as active demand are presented. Finally, regulatory issues of integrating electric vehicles into modern power systems are addressed. Inspired by two courses held under the EES...

Electrical Properties of an m × n Hammock Network

Science.gov (United States)

Tan, Zhen; Tan, Zhi-Zhong; Zhou, Ling

2018-05-01

Electrical property is an important problem in the field of natural science and physics, which usually involves potential, current and resistance in the electric circuit. We investigate the electrical properties of an arbitrary hammock network, which has not been resolved before, and propose the exact potential formula of an arbitrary m × n hammock network by means of the Recursion-Transform method with current parameters (RT-I) pioneered by one of us [Z. Z. Tan, Phys. Rev. E 91 (2015) 052122], and the branch currents and equivalent resistance of the network are derived naturally. Our key technique is to setting up matrix equations and making matrix transformation, the potential formula derived is a meaningful discovery, which deduces many novel applications. The discovery of potential formula of the hammock network provides new theoretical tools and techniques for related scientific research. Supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK20161278

Predicting musically induced emotions from physiological inputs: Linear and neural network models

Directory of Open Access Journals (Sweden)

Frank A. Russo

2013-08-01

Full Text Available Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of 'felt' emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants – heart rate, respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a nonlinear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The nonlinear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the nonlinear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Transfer of optical signals around bends in two-dimensional linear photonic networks

International Nuclear Information System (INIS)

Nikolopoulos, G M

2015-01-01

The ability to navigate light signals in two-dimensional networks of waveguide arrays is a prerequisite for the development of all-optical integrated circuits for information processing and networking. In this article, we present a theoretical analysis of bending losses in linear photonic lattices with engineered couplings, and discuss possible ways for their minimization. In contrast to previous work in the field, the lattices under consideration operate in the linear regime, in the sense that discrete solitons cannot exist. The present results suggest that the functionality of linear waveguide networks can be extended to operations that go beyond the recently demonstrated point-to-point transfer of signals, such as blocking, routing, logic functions, etc. (paper)

Facing a Problem of Electrical Energy Quality in Ship Networks-measurements, Estimation, Control

Institute of Scientific and Technical Information of China (English)

Tomasz Tarasiuk; Janusz Mindykowski; Xiaoyan Xu

2003-01-01

In this paper, electrical energy quality and its indices in ship electric networks are introduced, especially the meaning of electrical energy quality terms in voltage and active and reactive power distribution indices. Then methods of measurement of marine electrical energy indices are introduced in details and a microprocessor measurement-diagnosis system with the function of measurement and control is designed. Afterwards, estimation and control of electrical power quality of marine electrical power networks are introduced. And finally, according to the existing method of measurement and control of electrical power quality in ship power networks, the improvement of relative method is proposed.

NOTICE OF ELECTRICAL CUT - TEST OF THE SECURED NETWORK

CERN Multimedia

Electrical Service ST/EL

2001-01-01

The electrical service ST/EL will test the switching sequence between the secured network and the diesel generators on January 8, 2002. The normal network, general services of the sites Meyrin, Prevessin, SPS, Zone Nord, LHC1 and LHC18 will be cut between 6:00am and 6:10am. The secured network will be resupplied by the diesel generators after approximately 1 minute. The UPS network will not be affected. To facilitate the restart of the electrical network and to minimize the impact of the tests on critical equipment, we would like to ask you to stop any equipment that might suffer major inconveniences during the tests (e.g. computers). For any further information, please do not hesitate to contact the Technical Control Room TCR (72201) or G. Cumer (160592).

Optimal traffic control in highway transportation networks using linear programming

KAUST Repository

Li, Yanning; Canepa, Edward S.; Claudel, Christian G.

2014-01-01

of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can

Linear programming based on neural networks for radiotherapy treatment planning

International Nuclear Information System (INIS)

Xingen Wu; Limin Luo

2000-01-01

In this paper, we propose a neural network model for linear programming that is designed to optimize radiotherapy treatment planning (RTP). This kind of neural network can be easily implemented by using a kind of 'neural' electronic system in order to obtain an optimization solution in real time. We first give an introduction to the RTP problem and construct a non-constraint objective function for the neural network model. We adopt a gradient algorithm to minimize the objective function and design the structure of the neural network for RTP. Compared to traditional linear programming methods, this neural network model can reduce the time needed for convergence, the size of problems (i.e., the number of variables to be searched) and the number of extra slack and surplus variables needed. We obtained a set of optimized beam weights that result in a better dose distribution as compared to that obtained using the simplex algorithm under the same initial condition. The example presented in this paper shows that this model is feasible in three-dimensional RTP. (author)

Tissue characterization using electrical impedance spectroscopy data: a linear algebra approach.

Science.gov (United States)

Laufer, Shlomi; Solomon, Stephen B; Rubinsky, Boris

2012-06-01

In this study, we use a new linear algebra manipulation on electrical impedance spectroscopy measurements to provide real-time information regarding the nature of the tissue surrounding the needle in minimal invasive procedures. Using a Comsol Multiphysics three-dimensional model, a phantom based on ex vivo animal tissue and in vivo animal data, we demonstrate how tissue inhomogeneity can be characterized without any previous knowledge of the electrical properties of the different tissues, except that they should not be linearly dependent on a certain frequency range. This method may have applications in needle biopsies, radiation seeds, or minimally invasive surgery and can reduce the number of computer tomography or magnetic resonance imaging images. We conclude by demonstrating how this mathematical approach can be useful in other applications.

Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2009-01-01

With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

Linear electric machines, drives, and MAGLEVs handbook

CERN Document Server

Boldea, Ion

2013-01-01

Based on author Ion Boldea's 40 years of experience and the latest research, Linear Electric Machines, Drives, and Maglevs Handbook provides a practical and comprehensive resource on the steady improvement in this field. The book presents in-depth reviews of basic concepts and detailed explorations of complex subjects, including classifications and practical topologies, with sample results based on an up-to-date survey of the field. Packed with case studies, this state-of-the-art handbook covers topics such as modeling, steady state, and transients as well as control, design, and testing of li

A sequential Monte Carlo model of the combined GB gas and electricity network

International Nuclear Information System (INIS)

Chaudry, Modassar; Wu, Jianzhong; Jenkins, Nick

2013-01-01

A Monte Carlo model of the combined GB gas and electricity network was developed to determine the reliability of the energy infrastructure. The model integrates the gas and electricity network into a single sequential Monte Carlo simulation. The model minimises the combined costs of the gas and electricity network, these include gas supplies, gas storage operation and electricity generation. The Monte Carlo model calculates reliability indices such as loss of load probability and expected energy unserved for the combined gas and electricity network. The intention of this tool is to facilitate reliability analysis of integrated energy systems. Applications of this tool are demonstrated through a case study that quantifies the impact on the reliability of the GB gas and electricity network given uncertainties such as wind variability, gas supply availability and outages to energy infrastructure assets. Analysis is performed over a typical midwinter week on a hypothesised GB gas and electricity network in 2020 that meets European renewable energy targets. The efficacy of doubling GB gas storage capacity on the reliability of the energy system is assessed. The results highlight the value of greater gas storage facilities in enhancing the reliability of the GB energy system given various energy uncertainties. -- Highlights: •A Monte Carlo model of the combined GB gas and electricity network was developed. •Reliability indices are calculated for the combined GB gas and electricity system. •The efficacy of doubling GB gas storage capacity on reliability of the energy system is assessed. •Integrated reliability indices could be used to assess the impact of investment in energy assets

An analogue of Morse theory for planar linear networks and the generalized Steiner problem

International Nuclear Information System (INIS)

Karpunin, G A

2000-01-01

A study is made of the generalized Steiner problem: the problem of finding all the locally minimal networks spanning a given boundary set (terminal set). It is proposed to solve this problem by using an analogue of Morse theory developed here for planar linear networks. The space K of all planar linear networks spanning a given boundary set is constructed. The concept of a critical point and its index is defined for the length function l of a planar linear network. It is shown that locally minimal networks are local minima of l on K and are critical points of index 1. The theorem is proved that the sum of the indices of all the critical points is equal to χ(K)=1. This theorem is used to find estimates for the number of locally minimal networks spanning a given boundary set

Human networks in the European electric power industry

International Nuclear Information System (INIS)

Barjot, Dominique; Kurgan-van Hentenryk, Ginette

2004-01-01

Behind electrical systems, we should not forget the human networks. The European case is interesting for that matter. There were major players involved, from the pioneers up to the conceivers of national and international systems. More particularly, the engineers should be considered for their technical as well as organizational performance. Attitudes must also be stressed: in Europe, electricity has constantly been developed with both nationalist and internationalist considerations, as shown by the passage from Unternehmergeschaeft to Bankgeschaeft after 1918. Neither should we forget the role played by institutions in the formation of networks: schools, holdings, cartels, and also those frontier zones formed by small countries like Belgium and Switzerland. The human networks, finally, left long term results such as: interconnection, inter-firm cooperation, technocracy, and the growing intervention of the State

The impact of high PV penetration levels on electrical distribution networks

Energy Technology Data Exchange (ETDEWEB)

Collinson, A; Beddoes, A; Thornycroft, J [Halcrow (United Kingdom); Strbac, G; Jenkins, N [UMIST, Manchester (United Kingdom); Verhoeven, B [KEMA (Netherlands)

2002-07-01

This report describes the results of a collaborative study by EA Technology, UMIST and Halcrow into the effects of large-scale connection of dispersed photovoltaic (PV) power systems on the national electricity supply network. The report is intended to help manufacturers and installers of PV systems and electricity companies to understand the issues associated with grid connection of PV power systems. The increased use of PV systems is expected to have a significant impact on the design, operation and management of electricity supply networks. The study examined three main areas: probability and risk analysis of islanding; PV and network voltage control (including analysis of voltage control in a commercial, domestic retrofit and domestic new build scenarios); and future low voltage network design and operational policies.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Directory of Open Access Journals (Sweden)

Chandra Nagasuma R

2009-02-01

Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known

Tissue characterization using electrical impedance spectroscopy data: a linear algebra approach

International Nuclear Information System (INIS)

Laufer, Shlomi; Solomon, Stephen B; Rubinsky, Boris

2012-01-01

In this study, we use a new linear algebra manipulation on electrical impedance spectroscopy measurements to provide real-time information regarding the nature of the tissue surrounding the needle in minimal invasive procedures. Using a Comsol Multiphysics three-dimensional model, a phantom based on ex vivo animal tissue and in vivo animal data, we demonstrate how tissue inhomogeneity can be characterized without any previous knowledge of the electrical properties of the different tissues, except that they should not be linearly dependent on a certain frequency range. This method may have applications in needle biopsies, radiation seeds, or minimally invasive surgery and can reduce the number of computer tomography or magnetic resonance imaging images. We conclude by demonstrating how this mathematical approach can be useful in other applications. (paper)

Linear and nonlinear causality between sectoral electricity consumption and economic growth: Evidence from Taiwan

International Nuclear Information System (INIS)

Yang, Cheng-Lang; Lin, Hung-Pin; Chang, Chih-Heng

2010-01-01

This study investigates the linear and nonlinear causality between the total electricity consumption (TEC) and real gross domestic production (RGDP). Unlike previous literature, we solve the undetermined relation between RGDP and electricity consumption by classifying TEC into industrial sector consumption (ISC) and residential sector consumption (RSC) as well as investigating how TEC, ISC, and RSC influence Taiwan's RGDP. By using the Granger's linear causality test, it is shown that (i) there is a bidirectional causality among TEC, ISC, and RGDP, but a neutrality between RSC and RGDP with regard to the linear causality and (ii) there is still a bidirectional causality between TEC and RGDP, but a unidirectional causality between RSC and RGDP with regard to the nonlinear causality. On the basis of (i) and (ii), we suggest that the electricity policy formulators loosen the restriction on ISC and limit RSC in order to achieve the goal of economic growth.

A novel methodology for non-linear system identification of battery cells used in non-road hybrid electric vehicles

Science.gov (United States)

Unger, Johannes; Hametner, Christoph; Jakubek, Stefan; Quasthoff, Marcus

2014-12-01

An accurate state of charge (SoC) estimation of a traction battery in hybrid electric non-road vehicles, which possess higher dynamics and power densities than on-road vehicles, requires a precise battery cell terminal voltage model. This paper presents a novel methodology for non-linear system identification of battery cells to obtain precise battery models. The methodology comprises the architecture of local model networks (LMN) and optimal model based design of experiments (DoE). Three main novelties are proposed: 1) Optimal model based DoE, which aims to high dynamically excite the battery cells at load ranges frequently used in operation. 2) The integration of corresponding inputs in the LMN to regard the non-linearities SoC, relaxation, hysteresis as well as temperature effects. 3) Enhancements to the local linear model tree (LOLIMOT) construction algorithm, to achieve a physical appropriate interpretation of the LMN. The framework is applicable for different battery cell chemistries and different temperatures, and is real time capable, which is shown on an industrial PC. The accuracy of the obtained non-linear battery model is demonstrated on cells with different chemistries and temperatures. The results show significant improvement due to optimal experiment design and integration of the battery non-linearities within the LMN structure.

Virtual CO2 Emission Flows in the Global Electricity Trade Network.

Science.gov (United States)

Qu, Shen; Li, Yun; Liang, Sai; Yuan, Jiahai; Xu, Ming

2018-05-14

Quantifying greenhouse gas emissions due to electricity consumption is crucial for climate mitigation in the electric power sector. Current practices primarily use production-based emission factors to quantify emissions for electricity consumption, assuming production and consumption of electricity take place within the same region. The increasingly intensified cross-border electricity trade complicates the accounting for emissions of electricity consumption. This study employs a network approach to account for the flows in the whole electricity trade network to estimate CO 2 emissions of electricity consumption for 137 major countries/regions in 2014. Results show that in some countries, especially those in Europe and Southern Africa, the impacts of electricity trade on the estimation of emission factors and embodied emissions are significant. The changes made to emission factors by considering intergrid electricity trade can have significant implications for emission accounting and climate mitigation when multiplied by total electricity consumption of the corresponding countries/regions.

A new enhanced bat-inspired algorithm for finding linear supply function equilibrium of GENCOs in the competitive electricity market

International Nuclear Information System (INIS)

Niknam, Taher; Sharifinia, Sajjad; Azizipanah-Abarghooee, Rasoul

2013-01-01

Highlights: • Present optimal bidding strategies of Generating Companies (GENCOs) in a network-constrained electricity market. • Present new enhanced bat-inspired algorithm. • Consider the bi level optimization problem. • Present a linear supply function model. - Abstract: This paper proposes a new enhanced bat-inspired algorithm to find out linear supply function equilibrium of Generating Companies (GENCOs) in a network-constrained electricity market where they have incomplete information about other rivals. The model enables a GENCO to link its bidding price with the bidding quantity of its product. In this regard, the social welfare maximization is applied to clearing the market and nodal pricing mechanism is utilized to calculate the GENCO’s profit. It is formulated as a bi level optimization problem, where the higher level problem maximizes GENCO’s payoff and the lower level problem solves the independent system operator’s market clearing problem based on the maximization of social welfare. Due to non-convexity nature of the proposed bi level optimization problem, the mathematical-based optimization approach is incapable to solve the problem and obtain the nearly global optima. In order to overcome the obstacle of the conventional approaches, this study suggests a new meta-heuristic Bat-inspired Algorithm (BA) to achieve the nearly global solution of the bi level optimization problem. In addition a novel self-adaptive learning mechanism is utilized on the original BA to improve the population diversity and global searching capability. Numerical examples are applied to three test systems in order to evaluate the performances of the presented framework

Network Traffic Monitoring Using Poisson Dynamic Linear Models

Energy Technology Data Exchange (ETDEWEB)

Merl, D. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2011-05-09

In this article, we discuss an approach for network forensics using a class of nonstationary Poisson processes with embedded dynamic linear models. As a modeling strategy, the Poisson DLM (PoDLM) provides a very flexible framework for specifying structured effects that may influence the evolution of the underlying Poisson rate parameter, including diurnal and weekly usage patterns. We develop a novel particle learning algorithm for online smoothing and prediction for the PoDLM, and demonstrate the suitability of the approach to real-time deployment settings via a new application to computer network traffic monitoring.

Information and management system for the secondary electricity distribution network

Energy Technology Data Exchange (ETDEWEB)

Knezevic, M. (Rudnik i Termoelectrana Gacko u Osnivanju (Yugoslavia))

1988-07-01

Emphasizes the importance of a reliable and continuous secondary electrical distribution network for surface coal mine productivity. Interruptions in equipment operation caused by mechanical and electrical failures should be eliminated without delay. Effective communication systems should lead to reliable management and high productivity in mines. It is suggested that mines be divided into four groups according to their sensitivity to supply interruptions, and provided with remotely controlled signalling devices linked to main and auxiliary dispatching stations equipped with micro-computers. Productivity may be increased by some 50-70% and supply costs decreased by some 35% if appropriate electrical distribution systems are used. A sketch of a secondary electrical supply network is attached. 11 refs.

Magnetodynamic non-linearity of electric properties of uncompensated metals

International Nuclear Information System (INIS)

Sobol', V.R.; Mazurenko, O.N.

2001-01-01

Magnetodynamic non-linearity of electric properties of normal metals is investigated both experimentally and analytically provided that the drift of charge carriers of high density in crossed electric and magnetic fields results in generation of a self current field. The measurements were made on high purity polycrystalline aluminium cylindrical conductors under the action of the magnetic field, coaxial the sample axis, on the radial current. The electric potential and its nonlinear correction are determined in a wide range of energy dissipation values up to the levels corresponding to the crisis of liquid helium boiling. In the approximation of contribution additivity to the resistive effect of both the external and self magnetic field agreement between the experimental data and the results calculated using the macroscopic field equations is attained. The problems of magnetic energy concentration for cylindrical conductors is discussed in the approximation of long and short solenoids

INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

Directory of Open Access Journals (Sweden)

S. Saravanan

2012-07-01

Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

Forecasting electricity infeed for distribution system networks : an analysis of the Dutch case

NARCIS (Netherlands)

Tanrisever, F.; Derinkuyu, K.; Heeren, M.

2013-01-01

Estimating and managing electricity distribution losses are the core business competencies of distribution system operators (DSOs). Since electricity demand is a major driver of network losses, it is essential for DSOs to have an accurate estimate of the electricity infeed in their network. In this

THE EVALUATION OF THE EFFECT OF REACTIVE POWER COMPENSATION IN ELECTRIC POWER LOSSES IN ELECTRIC NETWORK OF RAILWAY JUNCTION

OpenAIRE

O. I. Bondar; I. L. Bondar

2009-01-01

In this work the generalized mathematical model of an electrical network of the electrified railway junction is proposed. An estimation of influence of static var compensators installation on electric power losses in a network is executed on the basis of given model.

AC Electric Field Communication for Human-Area Networking

Science.gov (United States)

Kado, Yuichi; Shinagawa, Mitsuru

We have proposed a human-area networking technology that uses the surface of the human body as a data transmission path and uses an AC electric field signal below the resonant frequency of the human body. This technology aims to achieve a “touch and connect” intuitive form of communication by using the electric field signal that propagates along the surface of the human body, while suppressing both the electric field radiating from the human body and mutual interference. To suppress the radiation field, the frequency of the AC signal that excites the transmitter electrode must be lowered, and the sensitivity of the receiver must be raised while reducing transmission power to its minimally required level. We describe how we are developing AC electric field communication technologies to promote the further evolution of a human-area network in support of ubiquitous services, focusing on three main characteristics, enabling-transceiver technique, application-scenario modeling, and communications quality evaluation. Special attention is paid to the relationship between electro-magnetic compatibility evaluation and regulations for extremely low-power radio stations based on Japan's Radio Law.

Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network

Directory of Open Access Journals (Sweden)

Changhong Deng

2016-11-01

Full Text Available Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

Co-Design Based Lateral Motion Control of All-Wheel-Independent-Drive Electric Vehicles with Network Congestion

Directory of Open Access Journals (Sweden)

Wanke Cao

2017-10-01

Full Text Available All-wheel-independent-drive electric vehicles (AWID-EVs have considerable advantages in terms of energy optimization, drivability and driving safety due to the remarkable actuation flexibility of electric motors. However, in their current implementations, various real-time data in the vehicle control system are exchanged via a controller area network (CAN, which causes network congestion and network-induced delays. These problems could lead to systemic instability and make the system integration difficult. The goal of this paper is to provide a design methodology that can cope with all these challenges for the lateral motion control of AWID-EVs. Firstly, a continuous-time model of an AWID-EV is derived. Then an expression for determining upper and lower bounds on the delays caused by CAN is presented and with which a discrete-time model of the closed-loop CAN system is derived. An expression on the bandwidth utilization is introduced as well. Thirdly, a co-design based scheme combining a period-dependent linear quadratic regulator (LQR and a dynamic period scheduler is designed for the resulting model and the stability criterion is also derived. The results of simulations and hard-in-loop (HIL experiments show that the proposed methodology can effectively guarantee the stability of the vehicle lateral motion control while obviously declining the network congestion.

The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach

Energy Technology Data Exchange (ETDEWEB)

Bessec, Marie [CGEMP, Universite Paris-Dauphine, Place du Marechal de Lattre de Tassigny Paris (France); Fouquau, Julien [LEO, Universite d' Orleans, Faculte de Droit, d' Economie et de Gestion, Rue de Blois, BP 6739, 45067 Orleans Cedex 2 (France)

2008-09-15

This paper investigates the relationship between electricity demand and temperature in the European Union. We address this issue by means of a panel threshold regression model on 15 European countries over the last two decades. Our results confirm the non-linearity of the link between electricity consumption and temperature found in more limited geographical areas in previous studies. By distinguishing between North and South countries, we also find that this non-linear pattern is more pronounced in the warm countries. Finally, rolling regressions show that the sensitivity of electricity consumption to temperature in summer has increased in the recent period. (author)

Throughput vs. Delay in Lossy Wireless Mesh Networks with Random Linear Network Coding

DEFF Research Database (Denmark)

Hundebøll, Martin; Pahlevani, Peyman; Roetter, Daniel Enrique Lucani

2014-01-01

This work proposes a new protocol applying on– the–fly random linear network coding in wireless mesh net- works. The protocol provides increased reliability, low delay, and high throughput to the upper layers, while being oblivious to their specific requirements. This seemingly conflicting goals ...

Pacemaker neuron and network oscillations depend on a neuromodulator-regulated linear current

Directory of Open Access Journals (Sweden)

Shunbing Zhao

2010-05-01

Full Text Available Linear leak currents have been implicated in the regulation of neuronal excitability, generation of neuronal and network oscillations, and network state transitions. Yet, few studies have directly tested the dependence of network oscillations on leak currents or explored the role of leak currents on network activity. In the oscillatory pyloric network of decapod crustaceans neuromodulatory inputs are necessary for pacemaker activity. A large subset of neuromodulators is known to activate a single voltage-gated inward current IMI, which has been shown to regulate the rhythmic activity of the network and its pacemaker neurons. Using the dynamic clamp technique, we show that the crucial component of IMI for the generation of oscillatory activity is only a close-to-linear portion of the current-voltage relationship. The nature of this conductance is such that the presence or the absence of neuromodulators effectively regulates the amount of leak current and the input resistance in the pacemaker neurons. When deprived of neuromodulatory inputs, pyloric oscillations are disrupted; yet, a linear reduction of the total conductance in a single neuron within the pacemaker group recovers not only the pacemaker activity in that neuron, but also leads to a recovery of oscillations in the entire pyloric network. The recovered activity produces proper frequency and phasing that is similar to that induced by neuromodulators. These results show that the passive properties of pacemaker neurons can significantly affect their capacity to generate and regulate the oscillatory activity of an entire network, and that this feature is exploited by neuromodulatory inputs.

Linear-control-based synchronization of coexisting attractor networks with time delays

International Nuclear Information System (INIS)

Yun-Zhong, Song

2010-01-01

This paper introduces the concept of linear-control-based synchronization of coexisting attractor networks with time delays. Within the new framework, closed loop control for each dynamic node is realized through linear state feedback around its own arena in a decentralized way, where the feedback matrix is determined through consideration of the coordination of the node dynamics, the inner connected matrix and the outer connected matrix. Unlike previously existing results, the feedback gain matrix here is decoupled from the inner matrix; this not only guarantees the flexible choice of the gain matrix, but also leaves much space for inner matrix configuration. Synchronization of coexisting attractor networks with time delays is made possible in virtue of local interaction, which works in a distributed way between individual neighbours, and the linear feedback control for each node. Provided that the network is connected and balanced, synchronization will come true naturally, where theoretical proof is given via a Lyapunov function. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme. (general)

Electrical localization of weakly electric fish using neural networks

International Nuclear Information System (INIS)

Kiar, Greg; Mamatjan, Yasin; Adler, Andy; Jun, James; Maler, Len

2013-01-01

Weakly Electric Fish (WEF) emit an Electric Organ Discharge (EOD), which travels through the surrounding water and enables WEF to locate nearby objects or to communicate between individuals. Previous tracking of WEF has been conducted using infrared (IR) cameras and subsequent image processing. The limitation of visual tracking is its relatively low frame-rate and lack of reliability when visually obstructed. Thus, there is a need for reliable monitoring of WEF location and behaviour. The objective of this study is to provide an alternative and non-invasive means of tracking WEF in real-time using neural networks (NN). This study was carried out in three stages. First stage was to recreate voltage distributions by simulating the WEF using EIDORS and finite element method (FEM) modelling. Second stage was to validate the model using phantom data acquired from an Electrical Impedance Tomography (EIT) based system, including a phantom fish and tank. In the third stage, the measurement data was acquired using a restrained WEF within a tank. We trained the NN based on the voltage distributions for different locations of the WEF. With networks trained on the acquired data, we tracked new locations of the WEF and observed the movement patterns. The results showed a strong correlation between expected and calculated values of WEF position in one dimension, yielding a high spatial resolution within 1 cm and 10 times higher temporal resolution than IR cameras. Thus, the developed approach could be used as a practical method to non-invasively monitor the WEF in real-time.

Private electricity consumption on the rise -- the impact of networking

International Nuclear Information System (INIS)

Aebischer, B.; Huser, A.

2001-01-01

This article discusses the effect of the networking of the various devices to be found in the average home and the trend towards increased electricity consumption that will be brought about by 'intelligent' houses. Different scenarios for the increase in electricity consumption due to the increased use of multimedia systems - from the personal computer and mobile phones to hi-fi systems and the Internet are discussed. The contrasting tendencies noted in this area - such as, for example, the use of electricity to operate systems that are used to optimise and thus reduce electricity consumption in general are also discussed. Also, indirect energy-reduction effects in other areas - such as traffic reduction as a result of tele-working - are examined. Results of simulations and prognoses made concerning future trends for the electricity consumption of the various devices in homes are presented and recommendations are made on how to keep electricity consumption low when networking domestic apparatus

Optimal Willingness to Supply Wholesale Electricity Under Asymmetric Linearized Marginal Costs

Directory of Open Access Journals (Sweden)

David Hudgins

2012-01-01

Full Text Available This analysis derives the profit-maximizing willingness to supply functions for single-plant and multi-plant wholesale electricity suppliers that all incur linear marginal costs. The optimal strategy must result in linear residual demand functions in the absence of capacity constraints. This necessarily leads to a linear pricing rule structure that can be used by firm managers to construct their offer curves and to serve as a benchmark to evaluate firm profit-maximizing behavior. The procedure derives the cost functions and the residual demand curves for merged or multi-plant generators, and uses these to construct the individual generator plant offer curves for a multi-plant firm.

Optimising reverse logistics network to support policy-making in the case of Electrical and Electronic Equipment.

Science.gov (United States)

Achillas, Ch; Vlachokostas, Ch; Aidonis, D; Moussiopoulos, N; Iakovou, E; Banias, G

2010-12-01

Due to the rapid growth of Waste Electrical and Electronic Equipment (WEEE) volumes, as well as the hazardousness of obsolete electr(on)ic goods, this type of waste is now recognised as a priority stream in the developed countries. Policy-making related to the development of the necessary infrastructure and the coordination of all relevant stakeholders is crucial for the efficient management and viability of individually collected waste. This paper presents a decision support tool for policy-makers and regulators to optimise electr(on)ic products' reverse logistics network. To that effect, a Mixed Integer Linear Programming mathematical model is formulated taking into account existing infrastructure of collection points and recycling facilities. The applicability of the developed model is demonstrated employing a real-world case study for the Region of Central Macedonia, Greece. The paper concludes with presenting relevant obtained managerial insights. Copyright © 2010 Elsevier Ltd. All rights reserved.

Energy-aware virtual network embedding in flexi-grid networks.

Science.gov (United States)

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng

2017-11-27

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

Science.gov (United States)

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

Development scheme for the public electricity transport network - 2006-2020

International Nuclear Information System (INIS)

2006-01-01

After having discussed the role of the development scheme and its mains requirements, presented its important components (energy needs, energy transport needs), and described its elaboration mode, this report gives an overview of the present status of the electricity transport network in France: 400.000 volts transport and interconnection networks, 225.000 volts and high voltage networks, development objectives, development context, transport network characteristics in 2006 (country gridding, development dynamics and consumption growth). Then, it presents a set of hypotheses about consumption, production and European exchanges. It identifies different types of constraints (customer connection, supply safety, electric and economic performance, robustness against extreme climate phenomena) and presents a method to assess these constraints (simulation of situations at risk, supply quality analysis, works expertise). The last part present the middle- and long-term constraints for the network

Supply curve bidding of electricity in constrained power networks

Energy Technology Data Exchange (ETDEWEB)

Al-Agtash, Salem Y. [Hijjawi Faculty of Engineering; Yarmouk University; Irbid 21163 (Jordan)

2010-07-15

This paper presents a Supply Curve Bidding (SCB) approach that complies with the notion of the Standard Market Design (SMD) in electricity markets. The approach considers the demand-side option and Locational Marginal Pricing (LMP) clearing. It iteratively alters Supply Function Equilibria (SFE) model solutions, then choosing the best bid based on market-clearing LMP and network conditions. It has been argued that SCB better benefits suppliers compared to fixed quantity-price bids. It provides more flexibility and better opportunity to achieving profitable outcomes over a range of demands. In addition, SCB fits two important criteria: simplifies evaluating electricity derivatives and captures smooth marginal cost characteristics that reflect actual production costs. The simultaneous inclusion of physical unit constraints and transmission security constraints will assure a feasible solution. An IEEE 24-bus system is used to illustrate perturbations of SCB in constrained power networks within the framework of SDM. By searching in the neighborhood of SFE model solutions, suppliers can obtain their best bid offers based on market-clearing LMP and network conditions. In this case, electricity producers can derive their best offering strategy both in the power exchange and the long-term contractual markets within a profitable, yet secure, electricity market. (author)

Supply curve bidding of electricity in constrained power networks

International Nuclear Information System (INIS)

Al-Agtash, Salem Y.

2010-01-01

This paper presents a Supply Curve Bidding (SCB) approach that complies with the notion of the Standard Market Design (SMD) in electricity markets. The approach considers the demand-side option and Locational Marginal Pricing (LMP) clearing. It iteratively alters Supply Function Equilibria (SFE) model solutions, then choosing the best bid based on market-clearing LMP and network conditions. It has been argued that SCB better benefits suppliers compared to fixed quantity-price bids. It provides more flexibility and better opportunity to achieving profitable outcomes over a range of demands. In addition, SCB fits two important criteria: simplifies evaluating electricity derivatives and captures smooth marginal cost characteristics that reflect actual production costs. The simultaneous inclusion of physical unit constraints and transmission security constraints will assure a feasible solution. An IEEE 24-bus system is used to illustrate perturbations of SCB in constrained power networks within the framework of SDM. By searching in the neighborhood of SFE model solutions, suppliers can obtain their best bid offers based on market-clearing LMP and network conditions. In this case, electricity producers can derive their best offering strategy both in the power exchange and the long-term contractual markets within a profitable, yet secure, electricity market. (author)

ON THE MANAGEMENT OF URBAN ELECTRIC NETWORKS IN THE CONDITIONS OF THE SMART GRID

Directory of Open Access Journals (Sweden)

M. А. Fursanov

2018-01-01

Full Text Available The issues of prospective operation of the city electric networks in the conditions of the MART GRID, which will be quite different as compared to the traditional understanding and approaches, are under consideration. This requires the selection and application of appropriate analytical criteria and approaches to assessment, analysis and control of the networks. With this regard the following criteria are recommended: in a particular case – the optimal (minimal technological electric power consumption (losses, while in general – economically reasonable (minimal cost value of electric power transmission. It should be also borne in mind that contemporary urban networks are actively saturated with distributed sources of small generation that have radically changed the structure of electrical networks; therefore, account for such sources is an absolutely necessary objective of management regimes of urban electric networks, both traditional and in associated with the SMART GRID. A case of the analysis and control of urban electric 10 kV networks with distributed small sources of generation has been developed and presented according to the theoretical criterion of minimum relative active power losses in the circuit as a control case. The conducted research makes it possible to determine the magnitude of the tolerance network mode from the point of the theoretical minimum. 

Pristine transfinite graphs and permissive electrical networks

CERN Document Server

Zemanian, Armen H

2001-01-01

A transfinite graph or electrical network of the first rank is obtained conceptually by connecting conventionally infinite graphs and networks together at their infinite extremities. This process can be repeated to obtain a hierarchy of transfiniteness whose ranks increase through the countable ordinals. This idea, which is of recent origin, has enriched the theories of graphs and networks with radically new constructs and research problems. The book provides a more accessible introduction to the subject that, though sacrificing some generality, captures the essential ideas of transfiniteness for graphs and networks. Thus, for example, some results concerning discrete potentials and random walks on transfinite networks can now be presented more concisely. Conversely, the simplifications enable the development of many new results that were previously unavailable. Topics and features: *A simplified exposition provides an introduction to transfiniteness for graphs and networks.*Various results for conventional g...

Outsourcing services in electricity distribution network industry; Ostopalveluiden kaeyttoe verkkoliiketoiminnassa

Energy Technology Data Exchange (ETDEWEB)

Aminoff, A.; Lappetelaeinen, I. (VTT Technical Research Centre of Finland, Espoo (Finland)); Partanen, J.; Viljainen, S.; Tahvanainen, K. (Lappeenranta Univ. of Technology (Finland)); Jaerventausta, P.; Trygg, P. (Tampere Univ. of Technology (Finland))

2009-02-15

This report examines purchased services in the electricity distribution industry. The report is specially directed to readers working in the industry or otherwise interested in it. This report is a result of a research study that was done in 2008 by VTT, Lappeenranta University of Technology and Tampere University of Technology. The authors are thankful for funders and companies that made this research possible and provided lot of information and knowledge. We appreciate the participants in the steering group as well as the companies and people who answered to questionnaires, gave interviews and took part in GDSSinnovation session. In the business of electricity distribution the usage of purchased services has been increasing during the past years and network companies have focused more on their core business processes. There are a couple of peaks in the number of new purchasing decisions in the middle of the 90s and in the beginning of 2000. The most popular purchased services are network construction and maintenance services. On the other hand, many network planning related activities are still done in-house by the network companies, and are considered their core business. There are some industry specific factors that affect to the decision on whether or not to buy the service outside the company and how to cooperate with the suppliers. For instance, many network companies are owned by municipalities and many service providers are owned by the network companies. The former issue may sometimes bring local politics into the decision-making of the network companies. The latter issue, in turn, has an impact on the relationship between the customer and the supplier, and the infra-organizational issues may sometimes complicate the service purchasing process. Electricity network companies also have natural monopoly positions in their operating areas. To prevent the abuse of monopoly positions, the network companies are subjected to economic regulation. This affects

Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks

DEFF Research Database (Denmark)

Heide, J; Zhang, Qi; Fitzek, F H P

2013-01-01

This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...... reduction in the number of transmitted packets can be achieved. However, NC introduces additional computations and potentially a non-negligible transmission overhead, both of which depend on the chosen coding parameters. Therefore it is necessary to consider the trade-off that these coding parameters...... present in order to obtain the lowest energy consumption per transmitted bit. This problem is analyzed and suitable coding parameters are determined for the popular Tmote Sky platform. Compared to the use of traditional RLNC, these parameters enable a reduction in the energy spent per bit which grows...

Electrifying Europe. The power of Europe in the construction of electricity networks

International Nuclear Information System (INIS)

Lagendijk, V.C.

2008-01-01

This book sets out to uncover the origins of the idea of a European electricity network. It explores historically the roots of a transnational European system, showing how engineers came to think in terms of 'Europe' already in the 1920s, and how these ideas continued to influence network-building in later decades. This thinking not only corresponded to economic and technical attributes of the system. This thesis claims that a European system was also legitimised by ideological motives. Covering the period between 1918 and 2001 the book provides a detailed analysis of ideas on, and the building of, a European electricity system. A substantial contribution is made to the much-neglected history of international collaboration in Europe. Prevailing histories of electricity infrastructures the focus is on national developments. Also, the history is placed in the broader historical context of the twentieth century, paying ample attention to the influence of both hot and cold wars, and interwar developments. By combining the specific history of this international collaboration with a more general political and economic history of the twentieth century, the author explains why a European solution emerged. The thesis primarily focuses on Western European developments and explains how this network took its specific shape through the building of different regional powerpools among national systems. In addition, the thesis presents a contribution to the emerging field of transnational history by focusing on the work and activities of international organisations, without neglecting the power and influence of nation-states. The book starts by revealing how an international community of electricity entrepreneurs and electrical engineers had existed since the turn of the century. Yet at the same time, national legislations came to limit the extent of international network development and operation. Whereas the first objections to these limitations were general, they became

Intelligent Electric Power Systems with Active-Adaptive Electric Networks: Challenges for Simulation Tools

Directory of Open Access Journals (Sweden)

Ufa Ruslan A.

2015-01-01

Full Text Available The motivation of the presented research is based on the needs for development of new methods and tools for adequate simulation of intelligent electric power systems with active-adaptive electric networks (IES including Flexible Alternating Current Transmission System (FACTS devices. The key requirements for the simulation were formed. The presented analysis of simulation results of IES confirms the need to use a hybrid modelling approach.

Bosch automotive electrics and automotive electronics systems and components, networking and hybrid drive

CERN Document Server

2014-01-01

The significance of electrical and electronic systems has increased considerably in the last few years and this trend is set to continue. The characteristics feature of innovative systems is the fact that they can work together in a network. This requires powerful bus systems that the electronic control units can use to exchange information. Networking and the various bus systems used in motor vehicles are the prominent new topic in the 5th edition of the "Automotive Electric, Automotive Electronics" technical manual. The existing chapters have also been updated, so that this new edition brings the reader up to date on the subjects of electrical and electronic systems in the motor vehicle. Content Electrical and electronical systems – Basic principles of networking - Examples of networked vehicles – Bus systems – Architecture of electronic systems – Mechatronics – Elektronics – Electronic control Units – Software – Sensors – Actuators – Hybrid drives – Vehicle electrical system – Start...

Information report on electricity distribution network security and financing

International Nuclear Information System (INIS)

2011-01-01

This report first outlines the degradation of electricity quality, and identifies the lack of investment as the main reason of the network weakness. It notices that the French network is much extended, and that the medium and low voltage networks need to be secured, and outlines that some legal measures have already been implemented to correct these problems. In its second part, the report comments the network manager's point of view, and denies his critics of the conceding authorities. It also discusses the network manager's investments, and finally formulates six propositions for a better future of the distribution network

Regulatory Improvements for Effective Integration of Distributed Generation into Electricity Distribution Networks

International Nuclear Information System (INIS)

Scheepers, M.J.J.; Jansen, J.C.; De Joode, J.; Bauknecht, D.; Gomez, T.; Pudjianto, D.; Strbac, G.; Ropenus, S.

2007-11-01

The growth of distributed electricity supply of renewable energy sources (RES-E) and combined heat and power (CHP) - so called distributed generation (DG) - can cause technical problems for electricity distribution networks. These integration problems can be overcome by reinforcing the network. Many European Member States apply network regulation that does not account for the impact of DG growth on the network costs. Passing on network integration costs to the DG-operator who is responsible for these extra costs may result in discrimination between different DG plants and between DG and large power generation. Therefore, in many regulatory systems distribution system operators (DSOs) are not being compensated for the DG integration costs. The DG-GRID project analysed technical and economical barriers for integration of distributed generation into electricity distribution networks. The project looked into the impact of a high DG deployment on the electricity distribution system costs and the impact on the financial position of the DSO. Several ways for improving network regulation in order to compensate DSOs for the increasing DG penetration were identified and tested. The DG-GRID project looked also into stimulating network innovations through economic regulation. The project was co-financed by the European Commission and carried out by nine European universities and research institutes. This report summarises the project results and is based on a number of DG-GRID reports that describe the conducted analyses and their results

APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

Energy Technology Data Exchange (ETDEWEB)

Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB

2012-11-01

Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks.

Directory of Open Access Journals (Sweden)

Sébastien Martin

Full Text Available Electrical Impedance Tomography (EIT is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort.In this paper, a post-processing technique based on an artificial neural network (ANN is proposed to obtain a nonlinear solution to the inverse problem, starting from a linear solution. While common reconstruction methods based on ANNs estimate the solution directly from the measured data, the method proposed here enhances the solution obtained from a linear solver.Applying a linear reconstruction algorithm before applying an ANN reduces the effects of noise and modeling errors. Hence, this approach significantly reduces the error associated with solving 2D inverse problems using machine-learning-based algorithms.This work presents radical enhancements in the stability of nonlinear methods for biomedical EIT applications.

Load management in electrical networks. Objectives, methods, prospects

International Nuclear Information System (INIS)

Gabioud, D.

2008-01-01

This illustrated article takes up the problems related to the variation of the load in electricity networks. How to handle the peak load? Different solutions in the energy demand management are discussed. Method based on the price, method based on the reduction of the load by electric utilities. Information systems are presented which gives the consumer the needed data to participate in the local load management.

Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model

OpenAIRE

Chassin, David P.; Posse, Christian

2004-01-01

The reliability of electric transmission systems is examined using a scale-free model of network structure and failure propagation. The topologies of the North American eastern and western electric networks are analyzed to estimate their reliability based on the Barabasi-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using s...

The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data

Directory of Open Access Journals (Sweden)

Yanan Liu

2016-10-01

Full Text Available There are many uncertainties and risks in residential electricity consumption associated with economic development. Knowledge of the relationship between residential electricity consumption and its key determinant—income—is important to the sustainable development of the electric power industry. Using panel data from 30 provinces for the 1995–2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of “prior restrictions”. Robust tests of different segment specifications and regression methods are performed to ensure the validity of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. Government should also emphasize electricity conservation in the industrial sector rather than in residential sector.

From electric networks to 'Smart grids'

International Nuclear Information System (INIS)

Hadjsaid, Nourredine; Sabonnadiere, Jean-Claude

2015-12-01

After decades of slow evolutions, and because of the emergence of renewable energies and of a multiplication of actors due to the liberalisation of energy markets, electric networks are entering a phase of large and complex development which will lead to a massive introduction of intelligence and to the building up of the 'smart grid' concept. The authors first identify the characteristics of the new energetic paradigm. The present operation of electric grids is based on four components: production by means of high power units installed in strategic locations, transport to consumption centres by means of a highly instrumented transport network which has highly centralised and hierarchical management, and consumers who are passive actors. They comment the implications of recent development for these three components. They describe how information and communication technologies (ICT) are used at the service of the grid, and how new technologies are integrated in different instruments (smart counter, actuators, fast cut devices, sensors, advanced supervision and control functions). Then they discuss the definition of a smart network or smart grid, the objectives it allows to be reached for energy transport as well as energy distribution. They discuss the desirable evolution of distribution networks and their technical objectives. Then, they give an overview of the various involved actors (consumers, network managers, electric equipment manufacturers, energy producers, and so on), evokes bodies and institutions involved in research on smart grids (notably in Grenoble within the INPG), give some examples of innovative concepts which are now being developed (intelligence distribution, virtual central station, grid monitoring, re-configurable grid, smart building). They also identify scientific and technological deadlocks, and outline the challenge of preparing the needed abilities for the development of smart grids

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.

Integrated planning of electric vehicles routing and charging stations location considering transportation networks and power distribution systems

Directory of Open Access Journals (Sweden)

Andrés Arias

2018-09-01

Full Text Available Electric Vehicles (EVs represent a significant option that contributes to improve the mobility and reduce the pollution, leaving a future expectation in the merchandise transportation sector, which has been demonstrated with pilot projects of companies operating EVs for products delivering. In this work a new approach of EVs for merchandise transportation considering the location of Electric Vehicle Charging Stations (EVCSs and the impact on the Power Distribution System (PDS is addressed. This integrated planning is formulated through a mixed integer non-linear mathematical model. Test systems of different sizes are designed to evaluate the model performance, considering the transportation network and PDS. The results show a trade-off between EVs routing, PDS energy losses and EVCSs location.

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

Control strategies for power distribution networks with electric vehicles integration

DEFF Research Database (Denmark)

Hu, Junjie

of electrical energy. A smart grid can also be dened as an electricity network that can intelligently integrate the actions of all users connected to it - generators, consumers and those that do both - in order to eciently deliver sustainable, economic and secure electricity supplies. This thesis focuses...... of the ii market. To build a complete solution for integration of EVs into the distribution network, a price coordinated hierarchical scheduling system is proposed which can well characterize the involved actors in the smart grid. With this system, we demonstrate that it is possible to schedule the charging......Demand side resources, like electric vehicles (EVs), can become integral parts of a smart grids because instead of just consuming power they are capable of providing valuable services to power systems. EVs can be used to balance the intermittent renewable energy resources such as wind and solar...

Robust Weak Chimeras in Oscillator Networks with Delayed Linear and Quadratic Interactions

Science.gov (United States)

Bick, Christian; Sebek, Michael; Kiss, István Z.

2017-10-01

We present an approach to generate chimera dynamics (localized frequency synchrony) in oscillator networks with two populations of (at least) two elements using a general method based on a delayed interaction with linear and quadratic terms. The coupling design yields robust chimeras through a phase-model-based design of the delay and the ratio of linear and quadratic components of the interactions. We demonstrate the method in the Brusselator model and experiments with electrochemical oscillators. The technique opens the way to directly bridge chimera dynamics in phase models and real-world oscillator networks.

Identifying options for regulating the coordination of network investments with investments in distributed electricity generation

International Nuclear Information System (INIS)

Nisten, E.

2010-02-01

The increase in the distributed generation of electricity, with wind turbines and solar panels, necessitates investments in the distribution network. The current tariff regulation in the Dutch electricity industry, with its ex post evaluation of the efficiency of investments and the frontier shift in the x-factor, delays these investments. In the unbundled electricity industry, the investments in the network need to be coordinated with those in the distributed generation of electricity to enable the DSOs to build enough network capacity. The current Dutch regulations do not provide for a sufficient information exchange between the generators and the system operators to coordinate the investments. This paper analyses these two effects of the Dutch regulation, and suggests improvements to the regulation of the network connection and transportation tariffs to allow for sufficient network capacity and coordination between the investments in the network and in the generation of electricity. These improvements include locally differentiated tariffs that increase with an increasing concentration of distributed generators.

Network investments and the integration of distributed generation: Regulatory recommendations for the Dutch electricity industry

International Nuclear Information System (INIS)

Niesten, Eva

2010-01-01

An increase in the distributed generation of electricity necessitates investments in the distribution network. The current tariff regulation in the Dutch electricity industry, with its ex post evaluation of the efficiency of investments, average benchmarking and a frontier shift in the x-factor, delays these investments. In the unbundled electricity industry, the investments in the network need to be coordinated with those in the distributed generation of electricity to enable the system operators to build enough network capacity. The current Dutch regulations do not provide for a sufficient information exchange between the generators and the system operators to coordinate the investments. This paper analyses these two effects of the Dutch regulations, and suggests improvements to the regulation of the network connection and transportation tariffs to allow for sufficient network capacity and coordination between the investments in the network and in the generation of electricity. These improvements include locally differentiated tariffs that increase with an increasing concentration of distributed generation.

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

Science.gov (United States)

Oprea, Simona-Vasilica; Pîrjan, Alexandru; Căruțașu, George; Petroșanu, Dana-Mihaela; Bâra, Adela; Stănică, Justina-Lavinia; Coculescu, Cristina

2018-05-05

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.

Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

Science.gov (United States)

Bakanovskaya, L. N.

2016-08-01

The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

Rational function systems and electrical networks with multiparameters

CERN Document Server

Lu, KaiSheng

2012-01-01

To overcome the problems of system theory and network theory over real field, this book uses matrices over the field F(z) of rational functions in multiparameters describing coefficient matrices of systems and networks and makes systems and network description over F(z) and researches their structural properties: reducible condition of a class of matrices over F(z) and their characteristic polynomial; type1 matrix and two basic properties; variable replacement conditions for independent parameters; structural controllability and observability of linear systems over F(z); separability, reducibi

Visible technologies, invisible organisations: An empirical study of public beliefs about electricity supply networks

International Nuclear Information System (INIS)

Devine-Wright, Patrick; Devine-Wright, Hannah; Sherry-Brennan, Fionnguala

2010-01-01

Reducing carbon emissions in the energy system poses significant challenges to electricity transmission and distribution networks. Whilst these challenges are as much social as economic or technical, to date few research studies have investigated public beliefs about electricity supply networks. This research aimed to address this gap by means of a nationally representative study of UK adults (n=1041), probing beliefs about how electricity reaches the home, responsibility for electricity supply, associations with the words 'National Grid', as well as beliefs about the planning of new infrastructure. Findings suggest that electricity networks are represented predominantly in terms of technologies rather than organisations, specifically in terms of familiar, visible components such as cables or wires, rather than more systemic concepts such as networks. Transmission and distribution network operators were largely invisible to members of the public. In terms of planning new lines, most respondents assumed that government ministers were involved in decision-making, while local residents were widely perceived to have little influence; moreover, there was strong public support for placing new power lines underground, regardless of the cost. In conclusion, organisational invisibility, coupled with low expectations of participatory involvement, could provoke public opposition and delay siting new network infrastructure.

Electric theory

International Nuclear Information System (INIS)

Gong, Ha Seong

2006-02-01

This book explains electric theory which is divided into four chapters. The first chapter includes electricity and material, electric field, capacitance, magnetic field and electromagnetic force, inductance. The second chapter mentions electronic circuit analysis, electric resistance,heating and power, chemical activity on current and battery with electrolysis. The third chapter deals with an alternating current circuit about the basics of an AC circuit, operating of resistance, inductance and capacitance, series circuit and parallel circuit of PLC, an alternating current circuit, Three-phase Alternating current, two terminal pair network and voltage and current of non-linearity circuit. The last explains transient phenomena of RC series circuit, RL series circuit, transient phenomena of an alternating current circuit and transient phenomena of RLC series circuit.

SYNCHRONIZATION OF NATIONAL GRID NETWORK WITH THE ELECTRICITY SHIPS NETWORK IN THE "SHORE TO SHIP" SYSTEM

Directory of Open Access Journals (Sweden)

Dariusz TARNAPOWICZ

2013-07-01

Full Text Available ‘Shore to ship’ system – ships’ power supply from the local electrical substations – is one of the effective ways to limit the negative impact of the ships lying in ports on the environment. Energy infrastructure of the port installation necessary to provide ships with power supply has to be designed so that different types of ships can use it. The important issue concerning ‘shore to ship’ system is the quality of power supply. This can be achieved via sustaining continuity of power supply while switching from the ships’ electrical network over to the national grid. In this article the author presents the way of synchronizing the national grid with the ships’ electrical network during ship’s lying in port. Such synchronization would allow for uninterruptible work of the ship’s electrical devices.

Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect.

Science.gov (United States)

Honjo, Keita; Shiraki, Hiroto; Ashina, Shuichi

2018-01-01

After the severe nuclear disaster in Fukushima, which was triggered by the Great East Japan earthquake in March 2011, nuclear power plants in Japan were temporarily shut down for mandatory inspections. To prevent large-scale blackouts, the Japanese government requested companies and households to reduce electricity consumption in summer and winter. It is reported that the domestic electricity demand had a structural decrease because of the electricity conservation effect (ECE). However, quantitative analysis of the ECE is not sufficient, and especially time variation of the ECE remains unclear. Understanding the ECE is important because Japan's NDC (nationally determined contribution) assumes the reduction of CO2 emissions through aggressive energy conservation. In this study, we develop a time series model of monthly electricity demand in Japan and estimate time variation of the ECE. Moreover, we evaluate the impact of electricity conservation on CO2 emissions from power plants. The dynamic linear model is used to separate the ECE from the effects of other irrelevant factors (e.g. air temperature, economic production, and electricity price). Our result clearly shows that consumers' electricity conservation behavior after the earthquake was not temporary but became established as a habit. Between March 2011 and March 2016, the ECE on industrial electricity demand ranged from 3.9% to 5.4%, and the ECE on residential electricity demand ranged from 1.6% to 7.6%. The ECE on the total electricity demand was estimated at 3.2%-6.0%. We found a seasonal pattern that the residential ECE in summer is higher than that in winter. The emissions increase from the shutdown of nuclear power plants was mitigated by electricity conservation. The emissions reduction effect was estimated at 0.82 MtCO2-2.26 MtCO2 (-4.5% on average compared to the zero-ECE case). The time-varying ECE is necessary for predicting Japan's electricity demand and CO2 emissions after the earthquake.

Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect.

Directory of Open Access Journals (Sweden)

Keita Honjo

Full Text Available After the severe nuclear disaster in Fukushima, which was triggered by the Great East Japan earthquake in March 2011, nuclear power plants in Japan were temporarily shut down for mandatory inspections. To prevent large-scale blackouts, the Japanese government requested companies and households to reduce electricity consumption in summer and winter. It is reported that the domestic electricity demand had a structural decrease because of the electricity conservation effect (ECE. However, quantitative analysis of the ECE is not sufficient, and especially time variation of the ECE remains unclear. Understanding the ECE is important because Japan's NDC (nationally determined contribution assumes the reduction of CO2 emissions through aggressive energy conservation. In this study, we develop a time series model of monthly electricity demand in Japan and estimate time variation of the ECE. Moreover, we evaluate the impact of electricity conservation on CO2 emissions from power plants. The dynamic linear model is used to separate the ECE from the effects of other irrelevant factors (e.g. air temperature, economic production, and electricity price. Our result clearly shows that consumers' electricity conservation behavior after the earthquake was not temporary but became established as a habit. Between March 2011 and March 2016, the ECE on industrial electricity demand ranged from 3.9% to 5.4%, and the ECE on residential electricity demand ranged from 1.6% to 7.6%. The ECE on the total electricity demand was estimated at 3.2%-6.0%. We found a seasonal pattern that the residential ECE in summer is higher than that in winter. The emissions increase from the shutdown of nuclear power plants was mitigated by electricity conservation. The emissions reduction effect was estimated at 0.82 MtCO2-2.26 MtCO2 (-4.5% on average compared to the zero-ECE case. The time-varying ECE is necessary for predicting Japan's electricity demand and CO2 emissions after the

Dynamic linear modeling of monthly electricity demand in Japan: Time variation of electricity conservation effect

Science.gov (United States)

Shiraki, Hiroto; Ashina, Shuichi

2018-01-01

After the severe nuclear disaster in Fukushima, which was triggered by the Great East Japan earthquake in March 2011, nuclear power plants in Japan were temporarily shut down for mandatory inspections. To prevent large-scale blackouts, the Japanese government requested companies and households to reduce electricity consumption in summer and winter. It is reported that the domestic electricity demand had a structural decrease because of the electricity conservation effect (ECE). However, quantitative analysis of the ECE is not sufficient, and especially time variation of the ECE remains unclear. Understanding the ECE is important because Japan’s NDC (nationally determined contribution) assumes the reduction of CO2 emissions through aggressive energy conservation. In this study, we develop a time series model of monthly electricity demand in Japan and estimate time variation of the ECE. Moreover, we evaluate the impact of electricity conservation on CO2 emissions from power plants. The dynamic linear model is used to separate the ECE from the effects of other irrelevant factors (e.g. air temperature, economic production, and electricity price). Our result clearly shows that consumers’ electricity conservation behavior after the earthquake was not temporary but became established as a habit. Between March 2011 and March 2016, the ECE on industrial electricity demand ranged from 3.9% to 5.4%, and the ECE on residential electricity demand ranged from 1.6% to 7.6%. The ECE on the total electricity demand was estimated at 3.2%–6.0%. We found a seasonal pattern that the residential ECE in summer is higher than that in winter. The emissions increase from the shutdown of nuclear power plants was mitigated by electricity conservation. The emissions reduction effect was estimated at 0.82 MtCO2–2.26 MtCO2 (−4.5% on average compared to the zero-ECE case). The time-varying ECE is necessary for predicting Japan’s electricity demand and CO2 emissions after the

Short-term electricity prices forecasting in a competitive market: A neural network approach

International Nuclear Information System (INIS)

Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

2007-01-01

This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

Directory of Open Access Journals (Sweden)

Ping-Huan Kuo

2018-04-01

Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

International program on linear electric motors

Energy Technology Data Exchange (ETDEWEB)

Dawson, G.E.; Eastham, A.R.; Parker, J.H.

1992-05-01

The International Program on Linear Electric Motors (LEM) was initiated for the purposes of commumication and coordination between various centers of expertise in LEM technology in Germany, Japan and Canada. Furthermore, it was intended to provide assessment and support of the planning of technological developments and for dissemination of information to researchers, service operators and policy makers, and to ensure that full advantage can be taken if opportunities for technology transfer occur. In the process, the program was able to provide closer contacts between researchers, to enhance and encourage collaborative research and development, and to facilitate joint ventures in advanced transportation technologies. Work done under the program is documented, and seminar materials presented by Canadian researchers in Italy, and by Italian researchers at Queen's University in Canada are presented. Five separate abstracts have been prepared for the main body of the report and the seminar materials.

Toward Model-Based Control of Non-linear Hydraulic Networks

DEFF Research Database (Denmark)

Tahavori, Maryamsadat; Jensen, Tom Nørgaard; Kallesøe, Carsten

2013-01-01

Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems (WSSs) for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power....... Following an analogy to electric circuits, first the mathematical expression for pressure drop over each component of the pipe network (WSS) such as pipes, pumps, valves and water towers is presented. Then the network model is derived based on the circuit theory and subsequently used for pressure management...

A generative modeling approach to connectivity-Electrical conduction in vascular networks

DEFF Research Database (Denmark)

Hald, Bjørn Olav

2016-01-01

The physiology of biological structures is inherently dynamic and emerges from the interaction and assembly of large collections of small entities. The extent of coupled entities complicates modeling and increases computational load. Here, microvascular networks are used to present a novel...... to synchronize vessel tone across the vast distances within a network. We hypothesize that electrical conduction capacity is delimited by the size of vascular structures and connectivity of the network. Generation and simulation of series of dynamical models of electrical spread within vascular networks...... of different size and composition showed that (1) Conduction is enhanced in models harboring long and thin endothelial cells that couple preferentially along the longitudinal axis. (2) Conduction across a branch point depends on endothelial connectivity between branches. (3) Low connectivity sub...

New approach in electricity network regulation: an issue on effective integration of distributed generation in electricity supply systems

International Nuclear Information System (INIS)

Scheepers, Martin J.J.; Wals, Adrian F.

2003-11-01

Technological developments and EU targets for penetration of renewable energy sources (RES) and greenhouse gas (GHG) reduction are decentralising the electricity infrastructure and services. Although, the liberalisation and internationalisation of the European electricity market has resulted in efforts to harmonise transmission pricing and regulation, hardly any initiative exists to consider the opening up and regulation of distribution networks to ensure effective participation of RES and distributed generation (DG) in the internal market. The SUSTELNET project has been created in order to close this policy gap. Its main objective is to develop regulatory roadmaps for the transition to an electricity market and network structure that creates a level playing field between centralised and decentralised generation and that facilitates the integration of RES, within the framework of the liberalisation of the EU electricity market. By analysing the technical, socio-economic and institutional dynamics of the European electricity system and markets, the project identifies the underlying patterns that provide the boundary conditions and levers for policy development to reach long term RES and GHG targets (2020-2030 time frame). This paper presents results of this analytical phase of the SUSTELNET project. Furthermore, preliminary results of the current work in progress are presented. Principles and criteria for a regulatory framework for sustainable electricity systems are discussed, as well as the development of medium to long-term transition strategies/roadmaps for network regulation and market transformation to facilitate the integration of RES and decentralised electricity generating systems.

A one-layer recurrent neural network for non-smooth convex optimization subject to linear inequality constraints

International Nuclear Information System (INIS)

Liu, Xiaolan; Zhou, Mi

2016-01-01

In this paper, a one-layer recurrent network is proposed for solving a non-smooth convex optimization subject to linear inequality constraints. Compared with the existing neural networks for optimization, the proposed neural network is capable of solving more general convex optimization with linear inequality constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds.

Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

OpenAIRE

Brown, Suzie; Johnson, Oliver; Tassi, Andrea

2018-01-01

Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kin...

History of electricity network control and distributed generation in the UK and Western Denmark

International Nuclear Information System (INIS)

Lehtonen, Markku; Nye, Sheridan

2009-01-01

Achieving the ambitious targets for renewable electricity generation in Europe will require harnessing a diverse range of energy sources, many of which are decentralised, small scale, and will be connected directly to the distribution networks. To control the two-way flows of electricity, the current passive network configurations will need to be replaced by active network management. This will require, in particular, innovations in intelligent IT-based network control. This paper draws on research on Large Technical Systems (LTS) and control systems in other sectors to analyse the evolution of electricity network control in western Denmark and the UK, since the Second World War. It concludes that lack of progress in network control has only recently-largely because of the combined needs to provide greater reliability and 'green' electricity within liberalised markets-emerged as a 'reverse salient' that will prevent the further development of the LTS of electricity supply industry towards desired direction. Breaking the inertia in the LTS and its control systems will require determined government action to promote learning and collaborative search for solutions. The UK might well draw lessons from the Danish pragmatism in fostering innovation through targeted support to collaborative R and D efforts towards sustainability objectives.

History of electricity network control and distributed generation in the UK and Western Denmark

Energy Technology Data Exchange (ETDEWEB)

Lehtonen, Markku [Sussex Energy Group, SPRU, University of Sussex, Freeman Centre, Falmer, Brighton, East Sussex BN1 9QE (United Kingdom); Nye, Sheridan [SPRU, University of Sussex, Freeman Centre, Falmer, Brighton, East Sussex BN1 9QE (United Kingdom)

2009-06-15

Achieving the ambitious targets for renewable electricity generation in Europe will require harnessing a diverse range of energy sources, many of which are decentralised, small scale, and will be connected directly to the distribution networks. To control the two-way flows of electricity, the current passive network configurations will need to be replaced by active network management. This will require, in particular, innovations in intelligent IT-based network control. This paper draws on research on Large Technical Systems (LTS) and control systems in other sectors to analyse the evolution of electricity network control in western Denmark and the UK, since the Second World War. It concludes that lack of progress in network control has only recently - largely because of the combined needs to provide greater reliability and 'green' electricity within liberalised markets - emerged as a 'reverse salient' that will prevent the further development of the LTS of electricity supply industry towards desired direction. Breaking the inertia in the LTS and its control systems will require determined government action to promote learning and collaborative search for solutions. The UK might well draw lessons from the Danish pragmatism in fostering innovation through targeted support to collaborative R and D efforts towards sustainability objectives. (author)

Learning oncogenetic networks by reducing to mixed integer linear programming.

Science.gov (United States)

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Gross domestic product estimation based on electricity utilization by artificial neural network

Science.gov (United States)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

Shortest loops are pacemakers in random networks of electrically coupled axons

Directory of Open Access Journals (Sweden)

Nikita eVladimirov

2012-04-01

Full Text Available High-frequency oscillations (HFOs are an important part of brain activity in health and disease. However, their origins remain obscure and controversial. One possible mechanism depends on the presence of sparsely distributed gap junctions that electrically couple the axons of principal cells. A plexus of electrically coupled axons is modeled as a random network with bidirectional connections between its nodes. Under certain conditions the network can demonstrate one of two types of oscillatory activity. Type I oscillations (100-200 Hz are predicted to be caused by spontaneously spiking axons in a network with strong (high-conductance gap junctions. Type II oscillations (200-300 Hz require no spontaneous spiking and relatively weak (low-conductance gap junctions, across which spike propagation failures occur. The type II oscillations are reentrant and self-sustained. Here we examine what determines the frequency of type II oscillations. Using simulations we show that the distribution of loop lengths is the key factor for determining frequency in type II network oscillations. We first analyze spike failure between two electrically coupled cells using a model of anatomically reconstructed CA1 pyramidal neuron. Then network oscillations are studied by a cellular automaton model with random network connectivity, in which we control loop statistics. We show that oscillation periods can be predicted from the network's loop statistics. The shortest loop, around which a spike can travel, is the most likely pacemaker candidate.The principle of one loop as a pacemaker is remarkable, because random networks contain a large number of loops juxtaposed and superimposed, and their number rapidly grows with network size. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We finally propose that type I oscillations may correspond to ripples, while type II oscillations correspond to so-called fast ripples.

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)

Vision and Strategy for Europe’s Electricity Networks of the Future

DEFF Research Database (Denmark)

Bamberger, Yves; Baptista, João; Belmans, Ronnie

remotely fromdemand centres. The energy challenges that Europe is now facing are changing the electricity generationlandscape. The drive for lower-carbon generation technologies, combined with greatly improved efficiency on the demand side, will enable customers to become much more inter......-active with the networks. More customer-centric networks are the way ahead, but these fundamental changes will impact significantly on network design and control. In this context, the European Technology Platform (ETP) SmartGrids was set up in 2005 to create a jointvision for the European networks of 2020 and beyond....... The platform includes representatives fromindustry, transmission and distribution system operators, research bodies and regulators. It has identified clear objectives and proposes an ambitious strategy to make a reality of this vision for the benefits of Europe and its electricity customers....

Improving distribution efficiency of electrical network using geo-electrical options. A case study in a rural area of Assam (India)

Energy Technology Data Exchange (ETDEWEB)

Hazarika, S.; Hiloidhari, M.; Baruah, D.C. [Energy Conservation Laboratory, Department of Energy, Tezpur University, Tezpur, Assam (India)

2012-11-15

Reduction of electricity distribution loss is a major area of focus in India. This paper aims to investigate geo-electrical options to improve distribution efficiency of electrical network. Distribution efficiencies corresponding to several possible electrical network options are assessed using Geographical Information System (GIS) integrated electrical theory. An existing electrical distribution network of a rural area in Assam (India) is considered for the present investigation. Information related to characteristics of loads, features of conductors, and transformers of the existing network are used for this investigation. The line losses of the three existing transformers are estimated at about 36, 20, and 3 % of their respective connected loads. Longer distribution lines associated with higher loads are the causes of higher line losses. Using basic electrical theory and GIS tools, it is found that line losses can be reduced in the existing distribution system through management of distribution transformer and reconductoring. Two alternative locations for each of the three transformers are identified for optimal management of distribution transformers. Similarly, five different types of commercially available conductors are identified for possible reconductoring to reduce line loss. The economic viability of reconductoring of distribution lines are also assessed through an economic analysis. Net present values of total expenditure comprising purchase prices of conductor and cost attributed to line losses are estimated considering 30 years of useful life. The existing conductor has the worst economic merit, though it is the cheapest amongst all. A net saving of about USD 24,084 is possible through the best choice of distribution conductor for the village.

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...

Software architecture for hybrid electrical/optical data center network

DEFF Research Database (Denmark)

Mehmeri, Victor; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso

2016-01-01

This paper presents hardware and software architecture based on Software-Defined Networking (SDN) paradigm and OpenFlow/NETCONF protocols for enabling topology management of hybrid electrical/optical switching data center networks. In particular, a development on top of SDN open-source controller...... OpenDaylight is presented to control an optical switching matrix based on Micro-Electro-Mechanical System (MEMS) technology....

Electricity Networks: Infrastructure and Operations. Too complex for a resource?

Energy Technology Data Exchange (ETDEWEB)

Volk, Dennis

2013-07-01

Electricity security remains a priority of energy policy and continuous electrification will further enhance the importance in the years to come. Market liberalisation has brought substantial benefits to societies, including competition, innovation, more client-oriented services and the reduced needs for public expenditure. Further, the path of decarbonisation is a must but experiences with many new technologies and policies show their many implications on power systems. Electricity networks form the backbone of reliable and affordable power systems and also significantly support the inception of renewable generation. The importance of distribution and transmission networks has to be well understood by policy makers and regulators to maintain the sensitive balance within the policy triangle of reliability, affordability and sustainability as power systems rapidly change. Failures in choosing the right institutions and regulatory frameworks to operate and build networks will put the sensitive balance within the policy triangle at risk. ''Too complex for a resource?'' identifies the key challenges the electricity distribution and transmission networks face today and in the future. It further provides for best practice examples on institutional design choices and regulatory frameworks for sound network service provision but also highlights the importance of additional responses required. More market-based and dynamic frameworks for various system services, the growing need for active service participation of renewable generators and highly independent and transparent central operators seem to be at the heart of these responses. ''Too complex for a resource?'' finds that the answer to the challenges ahead is not always more infrastructure and that networks and the services they provide have to be regarded as equal part of the total power system. Thus, accurate and dynamic cost allocation can significantly support to transform

Chemical networks with inflows and outflows: a positive linear differential inclusions approach.

Science.gov (United States)

Angeli, David; De Leenheer, Patrick; Sontag, Eduardo D

2009-01-01

Certain mass-action kinetics models of biochemical reaction networks, although described by nonlinear differential equations, may be partially viewed as state-dependent linear time-varying systems, which in turn may be modeled by convex compact valued positive linear differential inclusions. A result is provided on asymptotic stability of such inclusions, and applied to a ubiquitous biochemical reaction network with inflows and outflows, known as the futile cycle. We also provide a characterization of exponential stability of general homogeneous switched systems which is not only of interest in itself, but also plays a role in the analysis of the futile cycle. 2009 American Institute of Chemical Engineers

Application of local area networks to accelerator control systems at the Stanford Linear Accelerator

International Nuclear Information System (INIS)

Fox, J.D.; Linstadt, E.; Melen, R.

1983-03-01

The history and current status of SLAC's SDLC networks for distributed accelerator control systems are discussed. These local area networks have been used for instrumentation and control of the linear accelerator. Network topologies, protocols, physical links, and logical interconnections are discussed for specific applications in distributed data acquisition and control system, computer networks and accelerator operations

Electric field control methods for foil coils in high-voltage linear actuators

NARCIS (Netherlands)

Beek, van T.A.; Jansen, J.W.; Lomonova, E.A.

2015-01-01

This paper describes multiple electric field control methods for foil coils in high-voltage coreless linear actuators. The field control methods are evaluated using 2-D and 3-D boundary element methods. A comparison is presented between the field control methods and their ability to mitigate

An Industrial Control System for the Supervision of the CERN Electrical Distribution Network

CERN Document Server

Poulsen, S

1999-01-01

CERN operates a large distribution network for the supply of electricity to the particle accelerators, experiments and the associated infrastructure. The distribution network operates on voltage levels from 400 V to 400 kV with a total yearly consumption of near to 1000 GWh. In the past, the laboratory has developed an in-house control system for this network, using the technologies applied to the accelerator control system. However, CERN is now working on a project to purchase, configure and install an industrial Electrical Network Supervisor (ENS). This is a state-of-the-art industrial control system completely developed and supported by an external contractor. The system - based on a scalable and distributed architecture - will allow the installation to be performed gradually, and will be tested while the existing system is fully operational. Ultimately, the complete electrical distribution network will be supervised with this new system, the maintenance and further development of which will be the complet...

Analysis of reflectivity & predictability of electricity network tariff structures for household consumers

NARCIS (Netherlands)

Nijhuis, M.; Gibescu, M.; Cobben, J. F.G.

2017-01-01

Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric

A Cross-Layer Wireless Sensor Network Energy-Efficient Communication Protocol for Real-Time Monitoring of the Long-Distance Electric Transmission Lines

Directory of Open Access Journals (Sweden)

Jun Yu

2015-01-01

Full Text Available Optimization of energy consumption in Wireless Sensor Network (WSN nodes has become a critical link that constrains the engineering application of the smart grid due to the fact that the smart grid is characterized by long-distance transmission in a special environment. The paper proposes a linear hierarchical network topological structure specific to WSN energy conservation in environmental monitoring of the long-distance electric transmission lines in the smart grid. Based on the topological structural characteristics and optimization of network layers, the paper also proposes a Topological Structure be Layered Configurations (TSLC routing algorithm to improve the quality of WSN data transmission performance. Coprocessing of the network layer and the media access control (MAC layer is achieved by using the cross-layer design method, accessing the status for the nodes in the network layer and obtaining the status of the network nodes of the MAC layer. It efficiently saves the energy of the whole network, improves the quality of the network service performance, and prolongs the life cycle of the network.

Multiple synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses

International Nuclear Information System (INIS)

Liu, Chen; Wang, Jiang; Wang, Lin; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2014-01-01

Highlights: • Synchronization transitions in hybrid scale-free neuronal networks are investigated. • Multiple synchronization transitions can be induced by the time delay. • Effect of synchronization transitions depends on the ratio of the electrical and chemical synapses. • Coupling strength and the density of inter-neuronal links can enhance the synchronization. -- Abstract: The impacts of information transmission delay on the synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses are investigated. Numerical results show that multiple appearances of synchronization regions transitions can be induced by different information transmission delays. With the time delay increasing, the synchronization of neuronal activities can be enhanced or destroyed, irrespective of the probability of chemical synapses in the whole hybrid neuronal network. In particular, for larger probability of electrical synapses, the regions of synchronous activities appear broader with stronger synchronization ability of electrical synapses compared with chemical ones. Moreover, it can be found that increasing the coupling strength can promote synchronization monotonously, playing the similar role of the increasing the probability of the electrical synapses. Interestingly, the structures and parameters of the scale-free neuronal networks, especially the structural evolvement plays a more subtle role in the synchronization transitions. In the network formation process, it is found that every new vertex is attached to the more old vertices already present in the network, the more synchronous activities will be emerge

Linear induction accelerator and pulse forming networks therefor

Science.gov (United States)

Buttram, Malcolm T.; Ginn, Jerry W.

1989-01-01

A linear induction accelerator includes a plurality of adder cavities arranged in a series and provided in a structure which is evacuated so that a vacuum inductance is provided between each adder cavity and the structure. An energy storage system for the adder cavities includes a pulsed current source and a respective plurality of bipolar converting networks connected thereto. The bipolar high-voltage, high-repetition-rate square pulse train sets and resets the cavities.

Distributed generation system with PEM fuel cell for electrical power quality improvement

Energy Technology Data Exchange (ETDEWEB)

Ramirez, D.; Beites, L.F.; Blazquez, F. [Department of Electrical Engineering, ETSII, Escuela de Ingenieros Industriales, Universidad Politecnica de Madrid, C/ Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Ballesteros, J.C. [Endesa Generacion, S.A. c/ Ribera de Loira 60, 28042 Madrid (Spain)

2008-08-15

In this paper, a physical model for a distributed generation (DG) system with power quality improvement capability is presented. The generating system consists of a 5 kW PEM fuel cell, a natural gas reformer, hydrogen storage bottles and a bank of ultra-capacitors. Additional power quality functions are implemented with a vector-controlled electronic converter for regulating the injected power. The capabilities of the system were experimentally tested on a scaled electrical network. It is composed of different lines, built with linear inductances and resistances, and taking into account both linear and non-linear loads. The ability to improve power quality was tested by means of different voltage and frequency perturbations produced on the physical model electrical network. (author)

A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

International Nuclear Information System (INIS)

Mott, J.E.; King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D.

1992-01-01

The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system

A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

Energy Technology Data Exchange (ETDEWEB)

Mott, J.E. [Advanced Modeling Techniques Corp., Idaho Falls, ID (United States); King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D. [Argonne National Lab., Idaho Falls, ID (United States)

1992-03-06

The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system.

Application of static var compensator on large synchronous motors based on linear optimization control design

International Nuclear Information System (INIS)

Soltani, J.; Fath Abadi, A.M.

2003-01-01

This paper describes the application of static var compensators, on an electrical distribution network containing two large synchronous motors, one of which is excited via a three-phase thyristor bridge rectifier. The second machine is excited via a diode bridge rectifier. Based on linear optimization control, the measurable feedback signals are applied to the control system loops of static var compensators and the excitation control loop of the first synchronous motor. The phase equations method was used to develop a computer program to model the distribution network. Computer results were obtained to demonstrate the system performance for some abnormal modes of operation. These results show that employing static var compensators based on the linear optimization control design for electrical distribution networks containing large synchronous motors is beneficial and may be considered a first stage of the system design

Locating replenishment stations for electric vehicles: Application to Danish traffic data

DEFF Research Database (Denmark)

Wen, Min; Laporte, Gilbert; Madsen, Oli B.G.

2012-01-01

for electric vehicles on a traffic network with flow-based demand. The objective is to optimize the network performance, for example to maximize the flow covered by a prefixed number of stations, or to minimize the number of stations needed to cover traffic flows. Two mixed integer linear programming......Environment-friendly electric vehicles have gained substantial attention in governments, industry and universities. The deployment of a network of recharging stations is essential given their limited travel range. This paper considers the problem of locating electronic replenishment stations...

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...

An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

Science.gov (United States)

Ding, Lei; Xiao, Lin; Liao, Bolin; Lu, Rongbo; Peng, Hua

2017-01-01

To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new neural network for complex-valued systems of linear equation in complex domain is proposed and theoretically proved to be convergent within finite time. Then, the illustrative results show that the new neural network model has the higher precision and the higher convergence rate, as compared with the gradient neural network (GNN) model and the ZNN model. Finally, the application for controlling the robot using the proposed method for the complex-valued systems of linear equation is realized, and the simulation results verify the effectiveness and superiorness of the new neural network for the complex-valued systems of linear equation.

Monthly electric energy demand forecasting with neural networks and Fourier series

International Nuclear Information System (INIS)

Gonzalez-Romera, E.; Jaramillo-Moran, M.A.; Carmona-Fernandez, D.

2008-01-01

Medium-term electric energy demand forecasting is a useful tool for grid maintenance planning and market research of electric energy companies. Several methods, such as ARIMA, regression or artificial intelligence, have been usually used to carry out those predictions. Some approaches include weather or economic variables, which strongly influence electric energy demand. Economic variables usually influence the general series trend, while weather provides a periodic behavior because of its seasonal nature. This work investigates the periodic behavior of the Spanish monthly electric demand series, obtained by rejecting the trend from the consumption series. A novel hybrid approach is proposed: the periodic behavior is forecasted with a Fourier series while the trend is predicted with a neural network. Satisfactory results have been obtained, with a lower than 2% MAPE, which improve those reached when only neural networks or ARIMA were used for the same purpose. (author)

Trapped modes in linear quantum stochastic networks with delays

Energy Technology Data Exchange (ETDEWEB)

Tabak, Gil [Stanford University, Department of Applied Physics, Stanford, CA (United States); Mabuchi, Hideo

2016-12-15

Networks of open quantum systems with feedback have become an active area of research for applications such as quantum control, quantum communication and coherent information processing. A canonical formalism for the interconnection of open quantum systems using quantum stochastic differential equations (QSDEs) has been developed by Gough, James and co-workers and has been used to develop practical modeling approaches for complex quantum optical, microwave and optomechanical circuits/networks. In this paper we fill a significant gap in existing methodology by showing how trapped modes resulting from feedback via coupled channels with finite propagation delays can be identified systematically in a given passive linear network. Our method is based on the Blaschke-Potapov multiplicative factorization theorem for inner matrix-valued functions, which has been applied in the past to analog electronic networks. Our results provide a basis for extending the Quantum Hardware Description Language (QHDL) framework for automated quantum network model construction (Tezak et al. in Philos. Trans. R. Soc. A, Math. Phys. Eng. Sci. 370(1979):5270-5290, 2012) to efficiently treat scenarios in which each interconnection of components has an associated signal propagation time delay. (orig.)

Application of Nearly Linear Solvers to Electric Power System Computation

Science.gov (United States)

Grant, Lisa L.

To meet the future needs of the electric power system, improvements need to be made in the areas of power system algorithms, simulation, and modeling, specifically to achieve a time frame that is useful to industry. If power system time-domain simulations could run in real-time, then system operators would have situational awareness to implement online control and avoid cascading failures, significantly improving power system reliability. Several power system applications rely on the solution of a very large linear system. As the demands on power systems continue to grow, there is a greater computational complexity involved in solving these large linear systems within reasonable time. This project expands on the current work in fast linear solvers, developed for solving symmetric and diagonally dominant linear systems, in order to produce power system specific methods that can be solved in nearly-linear run times. The work explores a new theoretical method that is based on ideas in graph theory and combinatorics. The technique builds a chain of progressively smaller approximate systems with preconditioners based on the system's low stretch spanning tree. The method is compared to traditional linear solvers and shown to reduce the time and iterations required for an accurate solution, especially as the system size increases. A simulation validation is performed, comparing the solution capabilities of the chain method to LU factorization, which is the standard linear solver for power flow. The chain method was successfully demonstrated to produce accurate solutions for power flow simulation on a number of IEEE test cases, and a discussion on how to further improve the method's speed and accuracy is included.

Random linear network coding for streams with unequally sized packets

DEFF Research Database (Denmark)

Taghouti, Maroua; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk

2016-01-01

State of the art Random Linear Network Coding (RLNC) schemes assume that data streams generate packets with equal sizes. This is an assumption that results in the highest efficiency gains for RLNC. A typical solution for managing unequal packet sizes is to zero-pad the smallest packets. However, ...

Architecture, design and protection of electrical distribution networks

Energy Technology Data Exchange (ETDEWEB)

Sorrel, J.P. [Schneider electric Industries SA (France)

2000-07-01

Architectures related to AII Electric Ship (AES) require high level of propulsion power. Merchant ships and obviously warships require a low vulnerability, a high reliability and availability, a simple maintainability as well as an ordinary ode of operation. These constraints converge to an optimum single line diagram. We will focus on the mode of operation of the network, its constraints, the facilities to use a ring distribution for the ship service distribution system, the earthing of HV network as well as future developments. (author)

Incentive Regulation and Utility Benchmarking for Electricity Network Security

OpenAIRE

Zhang, Y.; Nepal, R.

2014-01-01

The incentive regulation of costs related to physical and cyber security in electricity networks is an important but relatively unexplored and ambiguous issue. These costs can be part of cost efficiency benchmarking or, alternatively, dealt with separately. This paper discusses the issues and proposes options for incorporating network security costs within incentive regulation in a benchmarking framework. The relevant concerns and limitations associated with the accounting and classification ...

A single charge in the actin binding domain of fascin can independently tune the linear and non-linear response of an actin bundle network.

Science.gov (United States)

Maier, M; Müller, K W; Heussinger, C; Köhler, S; Wall, W A; Bausch, A R; Lieleg, O

2015-05-01

Actin binding proteins (ABPs) not only set the structure of actin filament assemblies but also mediate the frequency-dependent viscoelastic moduli of cross-linked and bundled actin networks. Point mutations in the actin binding domain of those ABPs can tune the association and dissociation dynamics of the actin/ABP bond and thus modulate the network mechanics both in the linear and non-linear response regime. We here demonstrate how the exchange of a single charged amino acid in the actin binding domain of the ABP fascin triggers such a modulation of the network rheology. Whereas the overall structure of the bundle networks is conserved, the transition point from strain-hardening to strain-weakening sensitively depends on the cross-linker off-rate and the applied shear rate. Our experimental results are consistent both with numerical simulations of a cross-linked bundle network and a theoretical description of the bundle network mechanics which is based on non-affine bending deformations and force-dependent cross-link dynamics.

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network.

Science.gov (United States)

Gilra, Aditya; Gerstner, Wulfram

2017-11-27

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedforward and recurrent connections in a network of heterogeneous spiking neurons. The error in the output is fed back through fixed random connections with a negative gain, causing the network to follow the desired dynamics. The rule for Feedback-based Online Local Learning Of Weights (FOLLOW) is local in the sense that weight changes depend on the presynaptic activity and the error signal projected onto the postsynaptic neuron. We provide examples of learning linear, non-linear and chaotic dynamics, as well as the dynamics of a two-link arm. Under reasonable approximations, we show, using the Lyapunov method, that FOLLOW learning is uniformly stable, with the error going to zero asymptotically.

International program on linear electric motors. CIGGT report No. 92-1

Energy Technology Data Exchange (ETDEWEB)

Dawson, G.E.; Eastham, A.R.; Parker, J.H.

1992-12-31

The International Program for Linear Electric Motors (LEM) was begun in April 1989 to communicate and coordinate activities with centers of expertise in Germany, Canada, and Japan; to provide for the assessment and support of the planning of technological developments and for dissemination of information to researchers, service operators, and policy makers; and to ensure that full advantage can be taken if opportunities for technology transfer occur. This report documents the work done under the program, including standardizing linear induction motor (LIM) design characteristics; test procedures and measurement methods; rating; database for design data; criteria for evaluation of designs; computer programs for modelling performance; and a design study for an agreed application.

INTEGRAL INDEX OF OPERATION QUALITY FOR EVALUATION OF IMPACT OF DISTRIBUTIVE GENERATION SOURCES ON ELECTRIC NETWORK MODES

Directory of Open Access Journals (Sweden)

Petro D. Lezhniuk

2017-06-01

Full Text Available Method of operation quality evaluation of electric network, comprising renewable sources of energy (RSE is considered. Integral index that enables to evaluate the impact of RSE on energy losses and its quality as well as balance reliability in electric network is suggested. Mathematical model is constructed, taking into account the assumption that electric network with RSE may be in various operation modes, characterized by different technical economic indices. To determine the integral index of operation quality of electric network with RSE in all possible states tools of Markov processes theory and criterial method are used.

Reversible Control of Anisotropic Electrical Conductivity using Colloidal Microfluidic Networks

National Research Council Canada - National Science Library

Beskok, Ali; Bevan, Michael; Lagoudas, Dimitris; Ounaies, Zoubeida; Bahukudumbi, Pradipkumar; Everett, William

2007-01-01

This research addresses the tunable assembly of reversible colloidal structures within microfluidic networks to engineer multifunctional materials that exhibit a wide range of electrical properties...

Linearization Technologies for Broadband Radio-Over-Fiber Transmission Systems

Directory of Open Access Journals (Sweden)

Xiupu Zhang

2014-11-01

Full Text Available Linearization technologies that can be used for linearizing RoF transmission are reviewed. Three main linearization methods, i.e. electrical analog linearization, optical linearization, and electrical digital linearization are presented and compared. Analog linearization can be achieved using analog predistortion circuits, and can be used for suppression of odd order nonlinear distortion components, such as third and fifth order. Optical linearization includes mixed-polarization, dual-wavelength, optical channelization and the others, implemented in optical domain, to suppress both even and odd order nonlinear distortion components, such as second and third order. Digital predistortion has been a widely used linearization method for RF power amplifiers. However, digital linearization that requires analog to digital converter is severely limited to hundreds of MHz bandwidth. Instead, analog and optical linearization provide broadband linearization with up to tens of GHz. Therefore, for broadband radio over fiber transmission that can be used for future broadband cloud radio access networks, analog and optical linearization are more appropriate than digital linearization. Generally speaking, both analog and optical linearization are able to improve spur-free dynamic range greater than 10 dB over tens of GHz. In order for current digital linearization to be used for broadband radio over fiber transmission, the reduced linearization complexity and increased linearization bandwidth are required. Moreover, some digital linearization methods in which the complexity can be reduced, such as Hammerstein type, may be more promising and require further investigation.

New Operation and Maintenance Contract for Electrical Network

CERN Document Server

Kowalik, G

2001-01-01

The Electrical Exploitation is one of the few remaining operation services at CERN which nearly entirely relies on the CERN staff. Last year CERN policy, in connection with the LHC project needs, have led to the formulation of the strategy of out-sourcing of the Electrical Exploitation activities, market survey and subsequent Invitation to Tender. The following paper presents the approach used in the preparation of the Invitation to Tender and in solving of the out-sourcing issues applied to the operation and maintenance of the CERN electrical network. In particular the problems of the results oriented contract, quality assurance and performance as well requirement of the constantly increasing productivity of the Contractors team are treated. The paper gives also the particularities of the application of the out-sourcing to the electrical operation service as will as techniques used for the estimation of the work load of the activities being outsourced.

A neural network method for solving a system of linear variational inequalities

International Nuclear Information System (INIS)

Lan Hengyou; Cui Yishun

2009-01-01

In this paper, we transmute the solution for a new system of linear variational inequalities to an equilibrium point of neural networks, and by using analytic technique, some sufficient conditions are presented. Further, the estimation of the exponential convergence rates of the neural networks is investigated. The new and useful results obtained in this paper generalize and improve the corresponding results of recent works.

Fluid power network for centralized electricity generation in offshore wind farms

NARCIS (Netherlands)

Jarquin-Laguna, A.

2014-01-01

An innovative and completely different wind-energy conversion system is studied where a centralized electricity generation within a wind farm is proposed by means of a hydraulic network. This paper presents the dynamic interaction of two turbines when they are coupled to the same hydraulic network.

A network model for electrical transport in sea ice

International Nuclear Information System (INIS)

Zhu, J.; Golden, K.M.; Gully, A.; Sampson, C.

2010-01-01

Monitoring the thickness of sea ice is an important tool in assessing the impact of global warming on Earth's polar regions, and most methods of measuring ice thickness depend on detailed knowledge of its electrical properties. We develop a network model for the electrical conductivity of sea ice, which incorporates statistical measurements of the brine microstructure. The numerical simulations are in close agreement with direct measurements we made in Antarctica on the vertical conductivity of first year sea ice.

Economic planning for electric energy systems: a multi objective linearized approach for solution

International Nuclear Information System (INIS)

Mata Medeiros Branco, T. da.

1986-01-01

The economic planning problem associated to the expansion and operation of electrical power systems is considered in this study, represented for a vectorial objective function in which the minimization of resources involved and maximization of attended demand constitute goals to be satisfied. Supposing all the variables involved with linear characteristic and considering the conflict existing among the objectives to be achieved, in order to find a solution, a multi objective linearized approach is proposed. This approximation utilizes the compromise programming technique and linear programming methods. Generation and transmission are simultaneously considered into the optimization process in which associated losses and the capacity of each line are included. Illustrated examples are also presented with results discussed. (author)

MODELING OF SYMMETRIC THREE-PHASE ASYNCHRONOUS ELECTRIC MOTOR IN ASYMMETRIC CONNECTION TO NETWORK

Directory of Open Access Journals (Sweden)

V. I. Lukovnikov

2005-01-01

Full Text Available The paper shows how to solve the problem concerning reveal of changes in mathematical models and electric parameters of symmetric three-phase short-circuited asynchronous electric motors in case of their connection to single- or two-phase network in comparison with their connection to three-phase network. The uniform methodological approach permitting to generalize the known data and receive new results is offered in the paper.

Linear Matrix Inequalities in Multirate Control over Networks

Directory of Open Access Journals (Sweden)

Ángel Cuenca

2012-01-01

Full Text Available This paper faces two of the main drawbacks in networked control systems: bandwidth constraints and timevarying delays. The bandwidth limitations are solved by using multirate control techniques. The resultant multirate controller must ensure closed-loop stability in the presence of time-varying delays. Some stability conditions and a state feedback controller design are formulated in terms of linear matrix inequalities. The theoretical proposal is validated in two different experimental environments: a crane-based test-bed over Ethernet, and a maglev based platform over Profibus.

Tensions on the electric network; Tensions sur le reseau

Energy Technology Data Exchange (ETDEWEB)

Garrigues, B.

2001-10-01

Facing the potential 12000 MW of wind power projects, it is necessary to solve quickly the management of queues for the connection to the french electric network. This paper presents the today situation and proposes solutions. (A.L.B.)

Influence of the linear magneto-electric effect on the lateral shift of light reflected from a magneto-electric film

International Nuclear Information System (INIS)

Dadoenkova, Yu S; Petrov, R V; Bichurin, M I; Bentivegna, F F L; Dadoenkova, N N; Lyubchanskii, I L

2016-01-01

We present a theoretical investigation of the lateral shift of an infrared light beam reflected from a magnetic film deposited on a non-magnetic dielectric substrate, taking into account the linear magneto-electric interaction in the magnetic film. We use the stationary phase method to evaluate the lateral shift. It is shown that the magneto-electric coupling leads to a six-fold enhancement of the lateral shift amplitude of a p-(s-) polarized incident beam reflected into a s-(p-) polarized beam. A reversal of the magnetization in the film leads to a nonreciprocal sign change of the lateral shift. (paper)

Does electricity (and heat) network regulation have anything to learn from fixed line telecoms regulation?

International Nuclear Information System (INIS)

Pollitt, Michael

2010-01-01

The purpose of this paper is to examine the lessons from the recent history of telecoms deregulation for electricity (and by implication heat) network regulation. We do this in the context of Ofgem's RPI-X rate at 20 review of energy regulation in the UK, which considers whether RPI-X-based price regulation is fit for purpose after over 20 years of operation in energy networks. We examine the deregulation of fixed line telecoms in the UK and the lessons which it seems to suggest. We then apply the lessons to electricity networks in the context of a possible increase in distributed generation directly connected to local distribution networks. We conclude that there is the possibility of more parallels over time and suggest several implications of this for the regulation of electricity and heat networks. (author)

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

Science.gov (United States)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Electric network interconnection of Mashreq Arab Countries

International Nuclear Information System (INIS)

El-Amin, I.M.; Al-Shehri, A.M.; Opoku, G.; Al-Baiyat, S.A.; Zedan, F.M.

1994-01-01

Power system interconnection is a well established practice for a variety of technical and economical reasons. Several interconnected networks exist worldwide for a number of factors. Some of these networks cross international boundaries. This presentation discusses the future developments of the power systems of Mashreq Arab Countries (MAC). MAC consists of Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, United Arab Emirates (UAE), and Yemen. Mac power systems are operated by government or semigovernment bodies. Many of these countries have national or regional electric grids but are generally isolated from each other. With the exception of Saudi Arabia power systems, which employ 60 Hz, all other MAC utilities use 50 Hz frequency. Each country is served by one utility, except Saudi Arabia, which is served by four major utilities and some smaller utilities serving remote towns and small load centers. The major utilities are the Saudi Consolidated electric Company in the Eastern Province (SCECO East), SCECO Center, SCECO West, and SCECO South. These are the ones considered in this study. The energy resources in MAC are varied. Countries such as Egypt, Iraq, and Syria have significant hydro resources.The gulf countries and Iraq have abundant fossil fuel, The variation in energy resources as well as the characteristics of the electric load make it essential to look into interconnections beyond the national boundaries. Most of the existing or planned interconnections involve few power systems. A study involving 12 countries and over 20 utilities with different characteristics represents a very large scale undertaking

Measurement and analysis of the electric field radiation in pulsed power system of linear induction accelerator

International Nuclear Information System (INIS)

Cheng Qifeng; Ni Jianping; Meng Cui; Cheng Cheng; Liu Yinong; Li Jin

2009-01-01

The close of high voltage switch in pulsed power system of linear induction accelerator often radiates strong transient electric field, which may influence ambient sensitive electric equipment, signals and performance of other instruments, etc. By performing gridded measurement around the Marx generator, the general distribution law and basic characters of electric field radiation are summarized. The current signal of the discharge circuit is also measured, which demonstrates that the current and the radiated electric field both have a resonance frequency about 150 kHz, and contain much higher frequency components. (authors)

Electricity exchange and the valuation of transnational transmission access: A case study of intra-regional integration of the electric industries of Argentina and Chile

Science.gov (United States)

Brereton, Beverly Ann

The interconnection of neighboring electricity networks provides opportunities for the realization of synergies between electricity systems. Examples of the synergies to be realized are the rationalized management of the electricity networks whose fuel source domination differs, and the exploitation of non-coincident system peak demands. These factors allow technology diversity in the satisfaction of electricity demand, the coordination of planning and maintenance schedules between the networks by exploiting the cost differences in the pool of generation assets and the load configuration differences in the neighboring locations. The interconnection decision studied in this dissertation focused on the electricity networks of Argentina and Chile whose electricity systems operate in isolation at the current time. The cooperative game-theoretic framework was applied in the analysis of the decision facing the two countries and the net surplus to be derived from interconnection was evaluated. Measurement of the net gains from interconnection used in this study were reflected in changes in generating costs under the assumption that demand is fixed under all scenarios. With the demand for electricity assumed perfectly inelastic, passive or aggressive bidding strategies were considered under the scenarios for the generators in the two countries. The interconnection decision was modeled using a linear power flow model which utilizes linear programming techniques to reflect dispatch procedures based on generation bids. Results of the study indicate that the current interconnection project between Argentina and Chile will not result in positive net surplus under a variety of scenarios. Only under significantly reduced interconnection cost will the venture prove attractive. Possible sharing mechanisms were also explored in the research and a symmetric distribution of the net surplus to be derived under the reduced interconnection cost scenario was recommended to preserve equity

Modeling and analysis of a decentralized electricity market: An integrated simulation/optimization approach

International Nuclear Information System (INIS)

Sarıca, Kemal; Kumbaroğlu, Gürkan; Or, Ilhan

2012-01-01

In this study, a model is developed to investigate the implications of an hourly day-ahead competitive power market on generator profits, electricity prices, availability and supply security. An integrated simulation/optimization approach is employed integrating a multi-agent simulation model with two alternative optimization models. The simulation model represents interactions between power generator, system operator, power user and power transmitter agents while the network flow optimization model oversees and optimizes the electricity flows, dispatches generators based on two alternative approaches used in the modeling of the underlying transmission network: a linear minimum cost network flow model and a non-linear alternating current optimal power flow model. Supply, demand, transmission, capacity and other technological constraints are thereby enforced. The transmission network, on which the scenario analyses are carried out, includes 30 bus, 41 lines, 9 generators, and 21 power users. The scenarios examined in the analysis cover various settings of transmission line capacities/fees, and hourly learning algorithms. Results provide insight into key behavioral and structural aspects of a decentralized electricity market under network constraints and reveal the importance of using an AC network instead of a simplified linear network flow approach. -- Highlights: ► An agent-based simulation model with an AC transmission environment with a day-ahead market. ► Physical network parameters have dramatic effects over price levels and stability. ► Due to AC nature of transmission network, adaptive agents have more local market power than minimal cost network flow. ► Behavior of the generators has significant effect over market price formation, as pointed out by bidding strategies. ► Transmission line capacity and fee policies are found to be very effective in price formation in the market.

Forecasting of electricity prices with neural networks

Energy Technology Data Exchange (ETDEWEB)

Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

2006-08-15

During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

Forecasting of electricity prices with neural networks

International Nuclear Information System (INIS)

Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

2006-01-01

During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

Increasing penetration of renewable and distributed electricity generation and the need for different network regulation

International Nuclear Information System (INIS)

Joode, J. de; Jansen, J.C.; Welle, A.J. van der; Scheepers, M.J.J.

2009-01-01

The amount of decentralised electricity generation (DG) connected to distribution networks increases across EU member states. This increasing penetration of DG units poses potential costs and benefits for distribution system operators (DSOs). These DSOs are regulated since the business of electricity distribution is considered to be a natural monopoly. This paper identifies the impact of increasing DG penetration on the DSO business under varying parameters (network characteristics, DG technologies, network management type) and argues that current distribution network regulation needs to be improved in order for DSOs to continue to facilitate the integration of DG in the network. Several possible adaptations are analysed.

Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses

International Nuclear Information System (INIS)

Cofré, Rodrigo; Cessac, Bruno

2013-01-01

We investigate the effect of electric synapses (gap junctions) on collective neuronal dynamics and spike statistics in a conductance-based integrate-and-fire neural network, driven by Brownian noise, where conductances depend upon spike history. We compute explicitly the time evolution operator and show that, given the spike-history of the network and the membrane potentials at a given time, the further dynamical evolution can be written in a closed form. We show that spike train statistics is described by a Gibbs distribution whose potential can be approximated with an explicit formula, when the noise is weak. This potential form encompasses existing models for spike trains statistics analysis such as maximum entropy models or generalized linear models (GLM). We also discuss the different types of correlations: those induced by a shared stimulus and those induced by neurons interactions

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

Science.gov (United States)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

Markets in real electric networks require reactive prices

International Nuclear Information System (INIS)

Hogan, W.W.

1996-01-01

Extending earlier seminal work, the author finds that locational spot price differences in an electric network provide the natural measure of the appropriate internodal transport charge. However, the problem of loop flow requires different economic intuition for interpreting the implications of spot pricing. The Direct Current model, which is the usual approximation for estimating spot prices, ignores reactive power effects; this approximation is best when thermal constraints create network congestion. However, when voltage constraints are problematic, the DC Load model is insufficient; a full AC Model is required to determine both real and reactive spot prices. 16 figs., 3 tabs., 22 refs

Comparison of two biomass-electricity generation technologies in Peninsular Malaysia using linear programming method

International Nuclear Information System (INIS)

Kumaran, P.; Hari, Z.; Boosroh, M.H.

2006-01-01

Two technologies have been considered to generate electricity using palm oil mill waste, the Empty Fruit Bunch (EFB) as power plant fuel. One technology is to build new 100% EFB fired power plants, located in the vicinity of the palm oil mill, in which the produced electricity would be connected to the national electricity grid system. The other technology is to transport all the available EFB fuel to an existing coal power station in which the EFB fuel would be blended with coal and co-fired in conventional coal power plant to produce electricity. A study intended to compare the difference between these two technologies, to obtain the same electricity generation, has been done. Linear programming software was used simulate the two technologies to generate 5% of Peninsular Malaysia's electricity demand in the year 2005. The study indicated that the co firing technology total cost is 43.7% cheaper than EFB technology and the fuel coat is competitive until transport cost reaches 78 RM/tone

The deregulation of network industries: is the electricity sector an exception?

International Nuclear Information System (INIS)

Coppens, F.; Vivet, D.

2006-01-01

In view of their special characteristics, network industries have for quite a long time been considered as (natural) monopolies. Network externalities and economies of scale in particular justified this (natural) monopoly thesis. Since the last decade of the past century, however, a trend towards deregulation of such industries has been observed worldwide. This trend started with the successful introduction of competition in the telecommunications sector. The success in that sector is often used as an argument for opening up other network industries to competition as well. The study analyses whether this reasoning can be applied to the electricity sector. At first glance, the sectors electricity and telecommunications seem very similar, as both are network industries having been characterised for a long time by economies of scale, but for which technological progress may have put an end to this scale effect. However, the study points out that there are important differences. In the telecommunications sector, technological progress on the supply side has been accompanied by a strong growth in demand. There is no such demand side effect in the electricity branch. Moreover, due to physical characteristics, the electricity sector seems to be more complicated. In order to introduce competition, the sector has to be split up into sub-sectors (production, transmission, distribution and supply). Only two segments, production and supply, are considered as open markets; transmission and distribution, on the other hand, remain monopolies. This splitting up, however, entails a new kind of costs, the so-called transaction costs. As such the gains from the liberalization in certain segments might (partly) offset increase in the transaction costs resulting from vertical disintegration. (authors)

Network reconfiguration for loss reduction in electrical distribution system using genetic algorithm

International Nuclear Information System (INIS)

Adail, A.S.A.A.

2012-01-01

Distribution system is a critical links between the utility and the nuclear installation. During feeding electricity to that installation there are power losses. The quality of the network depends on the reduction of these losses. Distribution system which feeds the nuclear installation must have a higher quality power. For example, in Inshas site, electrical power is supplied to it from two incoming feeders (one from new abu-zabal substation and the other from old abu-zabal substation). Each feeder is designed to carry the full load, while the operator preferred to connect with a new abu-zabal substation, which has a good power quality. Bad power quality affects directly the nuclear reactor and has a negative impact on the installed sensitive equipment's of the operation. The thesis is Studying the electrical losses in a distribution system (causes and effected factors), feeder reconfiguration methods, and applying of genetic algorithm in an electric distribution power system. In the end, this study proposes an optimization technique based on genetic algorithms for distribution network reconfiguration to reduce the network losses to minimum. The proposed method is applied to IEEE test network; that contain 3 feeders and 16 nodes. The technique is applied through two groups, distribution have general loads, and nuclear loads. In the groups the technique applied to seven cases at normal operation state, system fault condition as well as different loads conditions. Simulated results are drawn to show the accuracy of the technique.

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)

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.

A Novel Linear Programming Formulation of Maximum Lifetime Routing Problem in Wireless Sensor Networks

DEFF Research Database (Denmark)

Cetin, Bilge Kartal; Prasad, Neeli R.; Prasad, Ramjee

2011-01-01

In wireless sensor networks, one of the key challenge is to achieve minimum energy consumption in order to maximize network lifetime. In fact, lifetime depends on many parameters: the topology of the sensor network, the data aggregation regime in the network, the channel access schemes, the routing...... protocols, and the energy model for transmission. In this paper, we tackle the routing challenge for maximum lifetime of the sensor network. We introduce a novel linear programming approach to the maximum lifetime routing problem. To the best of our knowledge, this is the first mathematical programming...

Effect of Enhanced Air Temperature (extreme heat, and Load of Non-Linear Against the Use of Electric Power

Directory of Open Access Journals (Sweden)

I Ketut Wijaya

2015-12-01

Full Text Available Usage Electric power is very easy to do, because the infrastructure for connecting  already available and widely sold. Consumption electric power is not accompanied by the ability to recognize electric power. The average increase of electricity power in Bali in extreme weather reaches 10% in years 2014, so that Bali suffered power shortages and PLN as the manager of electric power to perform scheduling on of electric power usage. Scheduling is done because many people use electric power as the load  of fan and Air Conditioner exceeding the previous time. Load of fan, air conditioning, and computers including non-linear loads which can add heat on the conductor of electricity. Non-linear load and hot weather can lead to heat on conductor so  insulation damaged  and cause electrical short circuit. Data of electric power obtained through questionnaires, surveys, measurement and retrieve data from various parties. Fires that occurred in 2014, namely 109 events, 44 is  event caused by an electric short circuit (approximately 40%. Decrease power factors can cause losses of electricity and hot. Heat can cause and adds heat on the  conductor electric. The analysis showed  understanding electric power of the average  is 27,700 with value between 20 to 40. So an understanding of the electrical power away from the understand so that many errors because of the act own. Installation tool ELCB very necessary but very necessary provide counseling   of electricity to the community.

A new neural network model for solving random interval linear programming problems.

Science.gov (United States)

Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza

2017-05-01

This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.

Thermal and Arc Flash Analysis of Electric Motor Drives in Distribution Networks

OpenAIRE

Nikolovski, Srete; Mlakić, Dragan; Alibašić, Emir

2017-01-01

The paper presents thermal analysis and arc flash analysis taking care of protection relays coordination settings for electric motor drives connected to the electrical network. Power flow analysis is performed to check if there are any voltage and loading violation conditions in the system. Fault analysis is performed to check the short circuit values and compute arc flash energy dissipated at industrial busbars to eliminate damage to electrical equipment and electrical shocks and hazard to p...

Linearized image reconstruction method for ultrasound modulated electrical impedance tomography based on power density distribution

International Nuclear Information System (INIS)

Song, Xizi; Xu, Yanbin; Dong, Feng

2017-01-01

Electrical resistance tomography (ERT) is a promising measurement technique with important industrial and clinical applications. However, with limited effective measurements, it suffers from poor spatial resolution due to the ill-posedness of the inverse problem. Recently, there has been an increasing research interest in hybrid imaging techniques, utilizing couplings of physical modalities, because these techniques obtain much more effective measurement information and promise high resolution. Ultrasound modulated electrical impedance tomography (UMEIT) is one of the newly developed hybrid imaging techniques, which combines electric and acoustic modalities. A linearized image reconstruction method based on power density is proposed for UMEIT. The interior data, power density distribution, is adopted to reconstruct the conductivity distribution with the proposed image reconstruction method. At the same time, relating the power density change to the change in conductivity, the Jacobian matrix is employed to make the nonlinear problem into a linear one. The analytic formulation of this Jacobian matrix is derived and its effectiveness is also verified. In addition, different excitation patterns are tested and analyzed, and opposite excitation provides the best performance with the proposed method. Also, multiple power density distributions are combined to implement image reconstruction. Finally, image reconstruction is implemented with the linear back-projection (LBP) algorithm. Compared with ERT, with the proposed image reconstruction method, UMEIT can produce reconstructed images with higher quality and better quantitative evaluation results. (paper)

Life cycle assessment of the Danish electricity distribution network

DEFF Research Database (Denmark)

Turconi, Roberto; Simonsen, Christian G.; Byriel, Inger P.

2014-01-01

Purpose This article provides life cycle inventory data for electricity distribution networks and a life cycle assessment (LCA) of the Danish transmission and distribution networks. The aim of the study was to evaluate the potential importance of environmental impacts associated with distribution...... complexity and material consumption. Infrastructure provided important contributions to metal depletion and freshwater eutrophication (copper and aluminum for manufacturing of the cables and associated recycling being the most important). Underground 50-kV lines had larger impacts than overhead lines, and 0...

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Science.gov (United States)

Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J.

2016-01-01

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology. PMID:26827811

Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

Science.gov (United States)

Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

2013-06-01

Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction

Directory of Open Access Journals (Sweden)

Lin JianJhong

2009-01-01

Full Text Available Szekeley observed that the dynamic pattern of the locomotion of salamanders can be explained by periodic vector sequences generated by logical neural networks. Such sequences can mathematically be described by "doubly periodic traveling waves" and therefore it is of interest to propose dynamic models that may produce such waves. One such dynamic network model is built here based on reaction-diffusion principles and a complete discussion is given for the existence of doubly periodic waves as outputs. Since there are 2 parameters in our model and 4 a priori unknown parameters involved in our search of solutions, our results are nontrivial. The reaction term in our model is a linear function and hence our results can also be interpreted as existence criteria for solutions of a nontrivial linear problem depending on 6 parameters.

Nonlinear aeroacoustic characterization of Helmholtz resonators with a local-linear neuro-fuzzy network model

Science.gov (United States)

Förner, K.; Polifke, W.

2017-10-01

The nonlinear acoustic behavior of Helmholtz resonators is characterized by a data-based reduced-order model, which is obtained by a combination of high-resolution CFD simulation and system identification. It is shown that even in the nonlinear regime, a linear model is capable of describing the reflection behavior at a particular amplitude with quantitative accuracy. This observation motivates to choose a local-linear model structure for this study, which consists of a network of parallel linear submodels. A so-called fuzzy-neuron layer distributes the input signal over the linear submodels, depending on the root mean square of the particle velocity at the resonator surface. The resulting model structure is referred to as an local-linear neuro-fuzzy network. System identification techniques are used to estimate the free parameters of this model from training data. The training data are generated by CFD simulations of the resonator, with persistent acoustic excitation over a wide range of frequencies and sound pressure levels. The estimated nonlinear, reduced-order models show good agreement with CFD and experimental data over a wide range of amplitudes for several test cases.

Receding horizon control of hybrid linear delayed systems: Application to sewer networks

OpenAIRE

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2013-01-01

A control-oriented hybrid linear model for water transport in sewer networks is proposed as a suitable framework for the computation of real-time controllers for the minimization of flooding in presence of heavy-rain events. The model is based on individual network elements (sewers, gates, weirs and tanks) and does not rely on topological simplifications, thus providing a better description of the hydrological and hydraulic phenomena than in similar works. Using a generic form of a hybrid lin...

Decompositions of injection patterns for nodal flow allocation in renewable electricity networks

Science.gov (United States)

Schäfer, Mirko; Tranberg, Bo; Hempel, Sabrina; Schramm, Stefan; Greiner, Martin

2017-08-01

The large-scale integration of fluctuating renewable power generation represents a challenge to the technical and economical design of a sustainable future electricity system. In this context, the increasing significance of long-range power transmission calls for innovative methods to understand the emerging complex flow patterns and to integrate price signals about the respective infrastructure needs into the energy market design. We introduce a decomposition method of injection patterns. Contrary to standard flow tracing approaches, it provides nodal allocations of link flows and costs in electricity networks by decomposing the network injection pattern into market-inspired elementary import/export building blocks. We apply the new approach to a simplified data-driven model of a European electricity grid with a high share of renewable wind and solar power generation.

Using adaptive network based fuzzy inference system to forecast regional electricity loads

International Nuclear Information System (INIS)

Ying, L.-C.; Pan, M.-C.

2008-01-01

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

Using adaptive network based fuzzy inference system to forecast regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

2008-02-15

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.

1990-07-01

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of Neural Network known as the multi-layer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author) 15 refs., 7 figs

Investigating solvability and complexity of linear active networks by means of matroids

DEFF Research Database (Denmark)

Petersen, Bjørn

1979-01-01

The solvability and complexity problems of finear active network are approached from a purely combinatorial point of view, using the concepts of matroid theory. Since the method is purely combinatorial, we take into account the network topology alone. Under this assumption necessary and sufficient...... conditions are given for the unique solvablity of linear active networks. The complexity and the number of dc-eigenfrequencies are also given. The method enables.you to decide if degeneracies are due to the topology alone, or if they are caused by special relations among network parameter values....... If the network parameter values are taken into account, the complexity and number of dc-eigenfrequencies given by the method, are only upper and lower bounds, respectively. The above conditions are fairly easily checked, and the complexity and number of dc-elgenfrequencies are found, using polynomially bounded...

Harmonics: Generation and Suppression in AC System Networks ...

African Journals Online (AJOL)

However, reactive power flow in electrical networks has adverse effects depending on their magnitude and the nature of the supply network. How these harmonics are generated by nonlinear loads and the means by which they can be kept low are the focus of this paper. Keywords: non-linear loads, harmonics, reactive ...

Simplified neural networks for solving linear least squares and total least squares problems in real time.

Science.gov (United States)

Cichocki, A; Unbehauen, R

1994-01-01

In this paper a new class of simplified low-cost analog artificial neural networks with on chip adaptive learning algorithms are proposed for solving linear systems of algebraic equations in real time. The proposed learning algorithms for linear least squares (LS), total least squares (TLS) and data least squares (DLS) problems can be considered as modifications and extensions of well known algorithms: the row-action projection-Kaczmarz algorithm and/or the LMS (Adaline) Widrow-Hoff algorithms. The algorithms can be applied to any problem which can be formulated as a linear regression problem. The correctness and high performance of the proposed neural networks are illustrated by extensive computer simulation results.

Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank.

Directory of Open Access Journals (Sweden)

Marco Bardoscia

Full Text Available We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of distress propagation is very simple: When a bank suffers a loss, distress propagates to its creditors, who in turn suffer losses, and so on. The original DebtRank assumes that losses are propagated linearly between connected banks. Here we relax this assumption and introduce a one-parameter family of non-linear propagation functions. As a case study, we apply this algorithm to a data-set of 183 European banks, and we study how the stability of the system depends on the non-linearity parameter under different stress-test scenarios. We find that the system is characterized by a transition between a regime where small shocks can be amplified and a regime where shocks do not propagate, and that the overall stability of the system increases between 2008 and 2013.

Online fouling detection in electrical circulation heaters using neural networks

Energy Technology Data Exchange (ETDEWEB)

Lalot, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Valenciennes (France). LME; Lecoeuche, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Lille (France). Laboratoire 13D

2003-06-01

Here is presented a method that is able to detect fouling during the service of a circulation electrical heater. The neural based technique is divided in two major steps: identification and classification. Each step uses a neural network, the connection weights of the first one being the inputs of the second network. Each step is detailed and the main characteristics and abilities of the two neural networks are given. It is shown that the method is able to discriminate fouling from viscosity modification that would lead to the same type of effect on the total heat transfer coefficient. (author)

The study of human bodies' impedance networks in testing leakage currents of electrical equipments

Science.gov (United States)

Zhang, Zhaohui; Wang, Xiaofei

2006-11-01

In the testing of electrical equipments' leakage currents, impedance networks of human bodies are used to simulate the current's effect on human bodies, and they are key to the preciseness of the testing result. This paper analyses and calculates three human bodies' impedance networks of measuring electric burn current, perception or reaction current, let-go current in IEC60990, by using Matlab, compares the research result of current effect thresholds' change with sine wave's frequency published in IEC479-2, and amends parameters of measuring networks. It also analyses the change of perception or reaction current with waveform by Multisim.

Design and analysis of electrical energy storage demonstration projects on UK distribution networks

International Nuclear Information System (INIS)

Lyons, P.F.; Wade, N.S.; Jiang, T.; Taylor, P.C.; Hashiesh, F.; Michel, M.; Miller, D.

2015-01-01

Highlights: • Results of an EES system demonstration project carried out in the UK. • Approaches to the design of trials for EES and observation on their application. • A formalised methodology for analysis of smart grids trials. • Validated models of energy storage. • Capability of EES to connect larger quantities of heat pumps and PV is evaluated. - Abstract: The UK government’s CO 2 emissions targets will require electrification of much of the country’s infrastructure with low carbon technologies such as photovoltaic panels, electric vehicles and heat pumps. The large scale proliferation of these technologies will necessitate major changes to the planning and operation of distribution networks. Distribution network operators are trialling electrical energy storage (EES) across their networks to increase their understanding of the contribution that it can make to enable the expected paradigm shift in generation and consumption of electricity. In order to evaluate a range of applications for EES, including voltage control and power flow management, installations have taken place at various distribution network locations and voltage levels. This article reports on trial design approaches and their application to a UK trial of an EES system to ensure broad applicability of the results. Results from these trials of an EES system, low carbon technologies and trial distribution networks are used to develop validated power system models. These models are used to evaluate, using a formalised methodology, the impact that EES could have on the design and operation of future distribution networks

Photodetachment of the H− ion in a linear time-dependent electric field

International Nuclear Information System (INIS)

Wang, De-Hua; Chen, Zhaohang; Cheng, Shaohao

2016-01-01

Using the time-dependent closed orbit theory, we study the photodetachment of the H − ion in a linear time-dependent electric field for the first time. An analytical formula for calculating the time-dependent photodetachment cross section of this system has been put forward. It is found when the external electric field changes very slowly with time, there is only one closed orbit of the detached electron and the photodetachment cross section is quite stable. However, when the electric field changes quickly with time, three different types of closed orbits are found and the photodetachment cross section oscillates in a much more complex way. The connection of each type of closed orbit with the oscillatory structure in the photodetachment cross section is analyzed quantitatively. In addition, the photon energy and the laser field parameters can also have great influence on the time-dependent photodetachment cross section of this system. This study provides a clear and intuitive picture for the photodetachment dynamics of a negative ion in the presence of a time-dependent electric field and may guide future experimental studies exploring the quantum effect in the photodetachment dynamics of negative ions from a time-dependent viewpoint. (paper)

A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle

Directory of Open Access Journals (Sweden)

Duo Zhang

2014-07-01

Full Text Available Vehicle active safety control is attracting ever increasing attention in the attempt to improve the stability and the maneuverability of electric vehicles. In this paper, a neural network combined inverse (NNCI controller is proposed, incorporating the merits of left-inversion and right-inversion. As the left-inversion soft-sensor can estimate the sideslip angle, while the right-inversion is utilized to decouple control. Then, the proposed NNCI controller not only linearizes and decouples the original nonlinear system, but also directly obtains immeasurable state feedback in constructing the right-inversion. Hence, the proposed controller is very practical in engineering applications. The proposed system is co-simulated based on the vehicle simulation package CarSim in connection with Matlab/Simulink. The results verify the effectiveness of the proposed control strategy.

Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks.

Science.gov (United States)

de Araújo, Paulo Régis C; Filho, Raimir Holanda; Rodrigues, Joel J P C; Oliveira, João P C M; Braga, Stephanie A

2018-04-24

At present, the standardisation of electrical equipment communications is on the rise. In particular, manufacturers are releasing equipment for the smart grid endowed with communication protocols such as DNP3, IEC 61850, and MODBUS. However, there are legacy equipment operating in the electricity distribution network that cannot communicate using any of these protocols. Thus, we propose an infrastructure to allow the integration of legacy electrical equipment to smart grids by using wireless sensor networks (WSNs). In this infrastructure, each legacy electrical device is connected to a sensor node, and the sink node runs a middleware that enables the integration of this device into a smart grid based on suitable communication protocols. This middleware performs tasks such as the translation of messages between the power substation control centre (PSCC) and electrical equipment in the smart grid. Moreover, the infrastructure satisfies certain requirements for communication between the electrical equipment and the PSCC, such as enhanced security, short response time, and automatic configuration. The paper’s contributions include a solution that enables electrical companies to integrate their legacy equipment into smart-grid networks relying on any of the above mentioned communication protocols. This integration will reduce the costs related to the modernisation of power substations.

Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Paulo Régis C. de Araújo

2018-04-01

Full Text Available At present, the standardisation of electrical equipment communications is on the rise. In particular, manufacturers are releasing equipment for the smart grid endowed with communication protocols such as DNP3, IEC 61850, and MODBUS. However, there are legacy equipment operating in the electricity distribution network that cannot communicate using any of these protocols. Thus, we propose an infrastructure to allow the integration of legacy electrical equipment to smart grids by using wireless sensor networks (WSNs. In this infrastructure, each legacy electrical device is connected to a sensor node, and the sink node runs a middleware that enables the integration of this device into a smart grid based on suitable communication protocols. This middleware performs tasks such as the translation of messages between the power substation control centre (PSCC and electrical equipment in the smart grid. Moreover, the infrastructure satisfies certain requirements for communication between the electrical equipment and the PSCC, such as enhanced security, short response time, and automatic configuration. The paper’s contributions include a solution that enables electrical companies to integrate their legacy equipment into smart-grid networks relying on any of the above mentioned communication protocols. This integration will reduce the costs related to the modernisation of power substations.

Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013

International Nuclear Information System (INIS)

Dev, Priya; Martin, Michael A.

2014-01-01

Highlights: • Neural nets are unable to properly capture spiky price behavior found in the electricity market. • We modeled electricity price data from the Australian National Electricity Market over 15 years. • Neural nets need to be augmented with other modeling techniques to capture price spikes. • We fit a Generalized Pareto Distribution to price spikes using a peaks-over-thresholds approach. - Abstract: Competitors in the electricity supply industry desire accurate predictions of electricity spot prices to hedge against financial risks. Neural networks are commonly used for forecasting such prices, but certain features of spot price series, such as extreme price spikes, present critical challenges for such modeling. We investigate the predictive capacity of neural networks for electricity spot prices using Australian National Electricity Market data. Following neural net modeling of the data, we explore extreme price spikes through extreme value modeling, fitting a Generalized Pareto Distribution to price peaks over an estimated threshold. While neural nets capture the smoother aspects of spot price data, they are unable to capture local, volatile features that characterize electricity spot price data. Price spikes can be modeled successfully through extreme value modeling

Network cost in transmission and distribution of electric power

International Nuclear Information System (INIS)

Lindahl, A.; Naeslund, B.; Oettinger-Biberg, C.; Olander, H.; Wuolikainen, T.; Fritz, P.

1994-01-01

This report is divided in two parts, where part 1 treats the charges on the regional nets with special emphasis on the net owners tariffs on a deregulated market. Part 2 describes the development of the network costs in electric power distribution for the period 1991-1993. 11 figs, 33 tabs

A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts

International Nuclear Information System (INIS)

Neaimeh, Myriam; Wardle, Robin; Jenkins, Andrew M.; Yi, Jialiang; Hill, Graeme; Lyons, Padraig F.; Hübner, Yvonne; Blythe, Phil T.; Taylor, Phil C.

2015-01-01

Highlights: • Working with unique datasets of EV charging and smart meter load demand. • Distribution networks are not a homogenous group with more capabilities to accommodate EVs than previously suggested. • Spatial and temporal diversity of EV charging demand alleviate the impacts on networks. • An extensive recharging infrastructure could enable connection of additional EVs on constrained distribution networks. • Electric utilities could increase the network capability to accommodate EVs by investing in recharging infrastructure. - Abstract: This work uses a probabilistic method to combine two unique datasets of real world electric vehicle charging profiles and residential smart meter load demand. The data was used to study the impact of the uptake of Electric Vehicles (EVs) on electricity distribution networks. Two real networks representing an urban and rural area, and a generic network representative of a heavily loaded UK distribution network were used. The findings show that distribution networks are not a homogeneous group with a variation of capabilities to accommodate EVs and there is a greater capability than previous studies have suggested. Consideration of the spatial and temporal diversity of EV charging demand has been demonstrated to reduce the estimated impacts on the distribution networks. It is suggested that distribution network operators could collaborate with new market players, such as charging infrastructure operators, to support the roll out of an extensive charging infrastructure in a way that makes the network more robust; create more opportunities for demand side management; and reduce planning uncertainties associated with the stochastic nature of EV charging demand.

Morphology and linear-elastic moduli of random network solids.

Science.gov (United States)

Nachtrab, Susan; Kapfer, Sebastian C; Arns, Christoph H; Madadi, Mahyar; Mecke, Klaus; Schröder-Turk, Gerd E

2011-06-17

The effective linear-elastic moduli of disordered network solids are analyzed by voxel-based finite element calculations. We analyze network solids given by Poisson-Voronoi processes and by the structure of collagen fiber networks imaged by confocal microscopy. The solid volume fraction ϕ is varied by adjusting the fiber radius, while keeping the structural mesh or pore size of the underlying network fixed. For intermediate ϕ, the bulk and shear modulus are approximated by empirical power-laws K(phi)proptophin and G(phi)proptophim with n≈1.4 and m≈1.7. The exponents for the collagen and the Poisson-Voronoi network solids are similar, and are close to the values n=1.22 and m=2.11 found in a previous voxel-based finite element study of Poisson-Voronoi systems with different boundary conditions. However, the exponents of these empirical power-laws are at odds with the analytic values of n=1 and m=2, valid for low-density cellular structures in the limit of thin beams. We propose a functional form for K(ϕ) that models the cross-over from a power-law at low densities to a porous solid at high densities; a fit of the data to this functional form yields the asymptotic exponent n≈1.00, as expected. Further, both the intensity of the Poisson-Voronoi process and the collagen concentration in the samples, both of which alter the typical pore or mesh size, affect the effective moduli only by the resulting change of the solid volume fraction. These findings suggest that a network solid with the structure of the collagen networks can be modeled in quantitative agreement by a Poisson-Voronoi process. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal residential smart appliances scheduling considering distribution network constraints

Directory of Open Access Journals (Sweden)

Yu-Ree Kim

2016-01-01

Full Text Available As smart appliances (SAs are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimization algorithms are developed using Mixed Integer Linear Programming, and the distribution network is solved by the Newton–Raphson AC power flow.

Fast non-linear extraction of plasma equilibrium parameters using a neural network mapping

International Nuclear Information System (INIS)

Lister, J.B.; Schnurrenberger, H.

1991-01-01

The shaping of non-circular plasmas requires a non-linear mapping between the measured diagnostic signals and selected equilibrium parameters. The particular configuration of neural network known as the multilayer perceptron provides a powerful and general technique for formulating an arbitrary continuous non-linear multi-dimensional mapping. This technique has been successfully applied to the extraction of equilibrium parameters from measurements of single-null diverted plasmas in the DIII-D tokamak; the results are compared with a purely linear mapping. The method is promising, and hardware implementation is straightforward. (author). 17 refs, 8 figs, 2 tab

The electrical network of maize root apex is gravity dependent.

Science.gov (United States)

Masi, Elisa; Ciszak, Marzena; Comparini, Diego; Monetti, Emanuela; Pandolfi, Camilla; Azzarello, Elisa; Mugnai, Sergio; Baluška, Frantisek; Mancuso, Stefano

2015-01-15

Investigations carried out on maize roots under microgravity and hypergravity revealed that gravity conditions have strong effects on the network of plant electrical activity. Both the duration of action potentials (APs) and their propagation velocities were significantly affected by gravity. Similarly to what was reported for animals, increased gravity forces speed-up APs and enhance synchronized electrical events also in plants. The root apex transition zone emerges as the most active, as well as the most sensitive, root region in this respect.

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

Science.gov (United States)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

A simple model for electrical charge in globular macromolecules and linear polyelectrolytes in solution

Science.gov (United States)

Krishnan, M.

2017-05-01

We present a model for calculating the net and effective electrical charge of globular macromolecules and linear polyelectrolytes such as proteins and DNA, given the concentration of monovalent salt and pH in solution. The calculation is based on a numerical solution of the non-linear Poisson-Boltzmann equation using a finite element discretized continuum approach. The model simultaneously addresses the phenomena of charge regulation and renormalization, both of which underpin the electrostatics of biomolecules in solution. We show that while charge regulation addresses the true electrical charge of a molecule arising from the acid-base equilibria of its ionizable groups, charge renormalization finds relevance in the context of a molecule's interaction with another charged entity. Writing this electrostatic interaction free energy in terms of a local electrical potential, we obtain an "interaction charge" for the molecule which we demonstrate agrees closely with the "effective charge" discussed in charge renormalization and counterion-condensation theories. The predictions of this model agree well with direct high-precision measurements of effective electrical charge of polyelectrolytes such as nucleic acids and disordered proteins in solution, without tunable parameters. Including the effective interior dielectric constant for compactly folded molecules as a tunable parameter, the model captures measurements of effective charge as well as published trends of pKa shifts in globular proteins. Our results suggest a straightforward general framework to model electrostatics in biomolecules in solution. In offering a platform that directly links theory and experiment, these calculations could foster a systematic understanding of the interrelationship between molecular 3D structure and conformation, electrical charge and electrostatic interactions in solution. The model could find particular relevance in situations where molecular crystal structures are not available or

Linear modeling of nonlinear systems using artificial neural networks based on I/O data and its application in power plant boiler modeling

International Nuclear Information System (INIS)

Ghaffari, A.; Nikkhah Bahrami, M.; Mohammadzaheri, M.

2005-01-01

In this paper a new method for linear modeling of nonlinear systems is presented. The method is based on the design of an artificial neural network with two layers. The network is trained only according to the input-output data of the system. The weights of connections in this network, represents the coefficients of the transfer function. For systems with linear behavior the method of least square error represents the best linear model of the system. However, for nonlinear systems, such as some subsystems in power plants boilers LSE does not represent the best linear approximation of the system, necessarily. In this paper a new linear modeling method is presented and applied to some subsystems in a power plant boiler. Comparison between the transfer function obtained in this way and by least square error method,shows that the neural network method gives better linear models for these nonlinear systems

RTE - Electricity transport network operator. Energy is our future: let's save it

International Nuclear Information System (INIS)

2014-01-01

Managing and developing the French electricity transport network is essential to provide quality electricity on a continuous basis to all consumers. Since it was founded in 2000, and since it was made into a public service company (2005), RTE has proven its ability to fulfil its public interest mission in complete security. In an open European electricity market, RTE is recognised for offering all of its customers fair access to its network, which is the first condition for healthy competition. Based on this and thanks to its investments and operating quality, RTE is constantly improving its performances to meet its customers', public authorities' and the Energy Regulation Committee's requirements. This public service action is focused on four strategic priorities: performance of industrial facilities; a human and managerial policy focused on skills and efficiency; sustainable development; professionalism and innovation. This brochure presents RTE's missions, company overview and European cooperation

Determinants of investment under incentive regulation: The case of the Norwegian electricity distribution networks

International Nuclear Information System (INIS)

Poudineh, Rahmatallah; Jamasb, Tooraj

2016-01-01

Investment in electricity networks, as regulated natural monopolies, is among the highest regulatory and energy policy priorities. The electricity sector regulators adopt different incentive mechanisms to ensure that the firms undertake sufficient investment to maintain and modernise the grid. Thus, an effective regulatory treatment of investment requires understanding the response of companies to the regulatory incentives. This study analyses the determinants of investment in electricity distribution networks using a panel dataset of 129 Norwegian companies observed from 2004 to 2010. A Bayesian Model Averaging approach is used to provide a robust statistical inference by taking into account the uncertainties around model selection and estimation. The results show that three factors drive nearly all network investments: investment rate in previous period, socio-economic costs of energy not supplied and finally useful life of assets. The results indicate that Norwegian companies have, to some degree, responded to the investment incentives provided by the regulatory framework. However, some of the incentives do not appear to be effective in driving the investments. - Highlights: • This paper investigates determinants of investment under incentive regulation. • We apply a Bayesian model averaging technique to deal with model uncertainty. • Dataset comprises 129 Norwegian electricity network companies from 2004 to 2010. • The results show that firms have generally responded to investment incentives. • However, some of the incentives do not appear to have been effective.

Modelling electric trains energy consumption using Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Martinez Fernandez, P.; Garcia Roman, C.; Insa Franco, R.

2016-07-01

Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways. (Author)

Smart PV grid to reinforce the electrical network

Science.gov (United States)

AL-Hamad, Mohamed Y.; Qamber, Isa S.

2017-11-01

Photovoltaic (PV) became the new competitive energy resources of the planet and needs to be engaged in grid to break up the congestion in both Distribution and Transmission systems. The objective of this research is to reduce the load flow through the distribution and transmission equipment by 20%. This reduction will help in relief networks loaded equipment's in all networks. Many projects are starting to develop in the GCC countries and need to be organized to achieve maximum benefits from involving the Renewable Energy Sources (RES) in the network. The GCC countries have a good location for solar energy with high intensity of the solar radiation and clear sky along the year. The opportunities of the solar energy is to utilize and create a sustainable energy resource for this region. Moreover, the target of this research is to engage the PV technology in such a way to lower the over loaded equipment and increases the electricity demand at the consumer's side.

An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

International Nuclear Information System (INIS)

Cai, Yuan; Wang, Jian-zhou; Tang, Yun; Yang, Yu-chen

2011-01-01

This paper presents a neural network based on adaptive resonance theory, named distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network), applied to the electric load forecasting problem. The distributed ART combines the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multi-layer perceptions. The HS-ARTMAP, a hybrid of an RBF (Radial Basis Function)-network-like module which uses hyper-sphere basis function substitute the Gaussian basis function and an ART-like module, performs incremental learning capabilities in function approximation problem. The HS-ARTMAP only receives the compressed distributed coding processed by distributed ART to deal with the proliferation problem which ARTMAP (adaptive resonance theory map) architecture often encounters and still performs well in electric load forecasting. To demonstrate the performance of the methodology, data from New South Wales and Victoria in Australia are illustrated. Results show that the developed method is much better than the traditional BP and single HS-ARTMAP neural network. -- Research highlights: → The processing of the presented network is based on compressed distributed data. It's an innovation among the adaptive resonance theory architecture. → The presented network decreases the proliferation the Fuzzy ARTMAP architectures usually encounter. → The network on-line forecasts electrical load accurately, stably. → Both one-period and multi-period load forecasting are executed using data of different cities.

Numerical Feedback Stabilization with Applications to Networks

Directory of Open Access Journals (Sweden)

Simone Göttlich

2017-01-01

Full Text Available The focus is on the numerical consideration of feedback boundary control problems for linear systems of conservation laws including source terms. We explain under which conditions the numerical discretization can be used to design feedback boundary values for network applications such as electric transmission lines or traffic flow systems. Several numerical examples illustrate the properties of the results for different types of networks.

Hybrid Electric Vehicle Experimental Model with CAN Network Real Time Control

Directory of Open Access Journals (Sweden)

RATOI, M.

2010-05-01

Full Text Available In this paper an experimental model with a distributed control system of a hybrid electrical vehicle is presented. A communication CAN network of high speed (1 Mbps assures a distributed control of the all components. The modeling and the control of different operating regimes are realized on an experimental test-bench of a hybrid electrical vehicle. The experimental results concerning the variations of the mains variables (currents, torques, speeds are presented.

Analysis of the electrical disturbances in CERN power distribution network with pattern mining methods

CERN Document Server

Abramenko, Oleksii

2017-01-01

The current research focuses on the perturbations within the electrical network of the LHC and its subsystems by analyzing measurements collected from oscilloscopes installed across different CERN sites, and alarms by electrical equipments. We analyze amplitude and duration of the glitches and, together with other relevant variables, correlate them with beam stopping events. The work also tries to identify assets affected by such perturbations using data mining and, in particular, frequent pattern mining methods. On the practical side we summarize results of our work by putting forward a prototype of a software tool enabling online monitoring of the alarms coming from the electrical network and facilitating glitch detection and analysis by a technical operator.

Business opportunities and dynamic competition through distributed generation in primary electricity distribution networks

International Nuclear Information System (INIS)

Raineri, R.; Rios, S.; Vasquez, R.

2005-01-01

In this paper, for a real electricity distribution network, an assessment of business opportunities to invest in distributed generation (DG) is performed through a simulation based on a full representation of three medium voltage (12 kV) feeders. The three feeders representation includes 1062 sections of conductors with 13 different sizes. The economic assessment focuses on both, the incentives of the incumbent distribution company and those of a new entrant. The technical and economic impact on losses, reliability and voltage regulation in the network area are verified. The DG solution analyzed determines a business opportunity for new investors where end users are also benefited. This work calls in the debate on the need to reformulate the current regulation model on electricity distribution, by defining clear rules to incorporate DG to the existing network, and to enable any agent to develop the proposed business. DG success depends on the location of adequate sites to strategically establish few DG units being a substitute to network expansion

Flexible bent rod model with a saturating induced dipole moment to study the electric linear dichroism of DNA fragments

Directory of Open Access Journals (Sweden)

Jorge A. Bertolotto

2016-06-01

Full Text Available In the present work we make a theoretical study of the steady state electric linear dichroism of DNA fragments in aqueous solution. The here developed theoretical approach considers a flexible bent rod model with a saturating induced dipole moment. The electric polarizability tensor of bent DNA fragments is calculated considering a phenomenological model which theoretical and experimental backgroung is presented here. The model has into account the electric polarizability longitudinal and transversal to the macroion. Molecular flexibility is described using an elastic potential. We consider DNA fragments originally bent with bending fluctuations around an average bending angle. The induced dipole moment is supposed constant once the electric field strength grows up at critical value. To calculate the reduced electric linear dichroism we determine the optical factor considering the basis of the bent DNA perpendicular to the molecular axis. The orientational distribution function has into account the anisotropic electric properties and the molecule flexibility. We applied the present theoretical background to fit electric dichroism experimental data of DNA fragments reported in the bibliography in a wide range of molecular weight and electric field. From these fits, values of DNA physical properties are estimated. We compare and discuss the results here obtained with the theoretical and experimental data presented by other authors. The original contributions of this work are: the inclusion of the transversal electric polarizability saturating with the electric field, the description of the electric properties with an electric polarizability tensor dependant on the bending angle and the use of an arc model originally bent.

Flexible bent rod model with a saturating induced dipole moment to study the electric linear dichroism of DNA fragments

Science.gov (United States)

Bertolotto, Jorge A.; Umazano, Juan P.

2016-06-01

In the present work we make a theoretical study of the steady state electric linear dichroism of DNA fragments in aqueous solution. The here developed theoretical approach considers a flexible bent rod model with a saturating induced dipole moment. The electric polarizability tensor of bent DNA fragments is calculated considering a phenomenological model which theoretical and experimental backgroung is presented here. The model has into account the electric polarizability longitudinal and transversal to the macroion. Molecular flexibility is described using an elastic potential. We consider DNA fragments originally bent with bending fluctuations around an average bending angle. The induced dipole moment is supposed constant once the electric field strength grows up at critical value. To calculate the reduced electric linear dichroism we determine the optical factor considering the basis of the bent DNA perpendicular to the molecular axis. The orientational distribution function has into account the anisotropic electric properties and the molecule flexibility. We applied the present theoretical background to fit electric dichroism experimental data of DNA fragments reported in the bibliography in a wide range of molecular weight and electric field. From these fits, values of DNA physical properties are estimated. We compare and discuss the results here obtained with the theoretical and experimental data presented by other authors. The original contributions of this work are: the inclusion of the transversal electric polarizability saturating with the electric field, the description of the electric properties with an electric polarizability tensor dependant on the bending angle and the use of an arc model originally bent.

Transmission investment in the Peruvian electricity market: Theory and applications

International Nuclear Information System (INIS)

Ruiz, Erix; Rosellón, Juan

2012-01-01

This research presents an application of the mechanism in (HRV) to promote electricity transmission network expansion in the Peruvian electricity transmission system known as SEIN (Sistema Eléctrico Interconectado Nacional). The HRV mechanism combines the merchant and regulatory approaches to promote investment into transmission grids. This mechanism gives incentives for efficient investment in expansion of the network by rebalancing over time the fixed and variable charges of a two-part tariff in the framework of a wholesale electricity market with locational pricing. The expansion of the network is carried out through the sale of Financial Transmission Rights (FTRs) for the congested lines. The mechanism is applied for 103 nodes of the SEIN using detailed characteristics of generators, nodes and transmission lines. Under Laspeyres weights and linear cost of expansion of transmission capacity, it is shown that prices converge to lower price levels as a result of increased transmission capacity.

CIRCUIT-DESIGN SOLUTIONS AND INFORMATION SUPPORT OF CITY ELECTRIC NETWORKS IN THE CONDITIONS OF THE SMART GRID

Directory of Open Access Journals (Sweden)

M. I. Fursanov

2017-01-01

Full Text Available The structure, circuit-design solutions and information support of the city electric networks in the conditions of the SMART GRID have been analyzed. It is demonstrated that the new conditions of functioning of electric power engineering, increasing demands for its technological state and reliability in most countries determined the transition to a restructuring of electrical networks to be based on the SMART GRID (intelligent power networks innovative new structure. The definitions of the SMART GRID, its various attributes and characteristics in most developed countries including Belarus are presented. It is revealed that the existing and future circuit and constructive solutions that can automate the process of managing modes of urban electric networks under the SMART GRID conditions are manifold. At present, the most common in distribution networks are the sources of distributed generation (combustion turbines, wind turbines, photovoltaic installations, mini-hydro, etc.. The patterns and problems of information traceability of a traditional urban networks of the unified energy system of Belarus have been analyzed, and it is demonstrated that in the conditions of the SMART GRID most of the problems of the control mode that are characteristic for traditional distribution networks 6–10 kV and 0.38 kV, lose their relevance. Therefore, the present article presents and features the main directions of development of automatic control modes of the SMART GRID.

Electricity network limitations on large-scale deployment of wind energy

Energy Technology Data Exchange (ETDEWEB)

Fairbairn, R.J.

1999-07-01

This report sought to identify limitation on large scale deployment of wind energy in the UK. A description of the existing electricity supply system in England, Scotland and Wales is given, and operational aspects of the integrated electricity networks, licence conditions, types of wind turbine generators, and the scope for deployment of wind energy in the UK are addressed. A review of technical limitations and technical criteria stipulated by the Distribution and Grid Codes, the effects of system losses, and commercial issues are examined. Potential solutions to technical limitations are proposed, and recommendations are outlined.

Index-aware model order reduction : LTI DAEs in electric networks

NARCIS (Netherlands)

Banagaaya, N.; Schilders, W.H.A.; Ali, G.; Tischendorf, C.

2014-01-01

Purpose Model order reduction (MOR) has been widely used in the electric networks but little has been done to reduce higher index differential algebraic equations (DAEs). The paper aims to discuss these issues. Design/methodology/approach Most methods first do an index reduction before reducing a

Bosch automotive electrics and automotive electronics. Systems and components, networking and hybrid drive. 5. ed.

Energy Technology Data Exchange (ETDEWEB)

NONE

2014-07-01

Complete reference guide to automotive electrics and electronics. The significance of electrical and electronic systems has increased considerably in the last few years and this trend is set to continue. The characteristics feature of innovative systems is the fact that they can work together in a network. This requires powerful bus systems that the electronic control units can use to exchange information. Networking and the various bus systems used in motor vehicles are the prominent new topic in the 5th edition of the ''Automotive Electric, Automotive Electronics'' technical manual. The existing chapters have also been updated, so that this new edition brings the reader up to date on the subjects of electrical and electronic systems in the motor vehicle.

Evaluating the electricity intensity of evolving water supply mixes: the case of California’s water network

Science.gov (United States)

Stokes-Draut, Jennifer; Taptich, Michael; Kavvada, Olga; Horvath, Arpad

2017-11-01

Climate change is making water supply less predictable, even unreliable, in parts of the world. Urban water providers, especially in already arid areas, will need to diversify their water resources by switching to alternative sources and negotiating trading agreements to create more resilient and interdependent networks. The increasing complexity of these networks will likely require more operational electricity. The ability to document, visualize, and analyze water-energy relationships will be critical to future water planning, especially as data needed to conduct the analyses become increasingly available. We have developed a network model and decision-support tool, WESTNet, to perform these tasks. Herein, WESTNet was used to analyze a model of California’s 2010 urban water network as well as the projected system for 2020 and 2030. Results for California’s ten hydrologic regions show that the average number of water sources per utility and total electricity consumption for supplying water will increase in spite of decreasing per-capita water consumption. Electricity intensity (kWh m-3) will increase in arid regions of the state due to shifts to alternative water sources such as indirect potable water reuse, desalination, and water transfers. In wetter, typically less populated, regions, reduced water demand for electricity-intensive supplies will decrease the electricity intensity of the water supply mix, though total electricity consumption will increase due to urban population growth. The results of this study provide a baseline for comparing current and potential innovations to California’s water system. The WESTNet tool can be applied to diverse water systems in any geographic region at a variety of scales to evaluate an array of network-dependent water-energy parameters.

The Use of Artificial Neural Networks for Forecasting the Electric Demand of Stand-Alone Consumers

Science.gov (United States)

Ivanin, O. A.; Direktor, L. B.

2018-05-01

The problem of short-term forecasting of electric power demand of stand-alone consumers (small inhabited localities) situated outside centralized power supply areas is considered. The basic approaches to modeling the electric power demand depending on the forecasting time frame and the problems set, as well as the specific features of such modeling, are described. The advantages and disadvantages of the methods used for the short-term forecast of the electric demand are indicated, and difficulties involved in the solution of the problem are outlined. The basic principles of arranging artificial neural networks are set forth; it is also shown that the proposed method is preferable when the input information necessary for prediction is lacking or incomplete. The selection of the parameters that should be included into the list of the input data for modeling the electric power demand of residential areas using artificial neural networks is validated. The structure of a neural network is proposed for solving the problem of modeling the electric power demand of residential areas. The specific features of generation of the training dataset are outlined. The results of test modeling of daily electric demand curves for some settlements of Kamchatka and Yakutia based on known actual electric demand curves are provided. The reliability of the test modeling has been validated. A high value of the deviation of the modeled curve from the reference curve obtained in one of the four reference calculations is explained. The input data and the predicted power demand curves for the rural settlement of Kuokuiskii Nasleg are provided. The power demand curves were modeled for four characteristic days of the year, and they can be used in the future for designing a power supply system for the settlement. To enhance the accuracy of the method, a series of measures based on specific features of a neural network's functioning are proposed.

Dynamics of global supply chain and electric power networks: Models, pricing analysis, and computations

Science.gov (United States)

Matsypura, Dmytro

In this dissertation, I develop a new theoretical framework for the modeling, pricing analysis, and computation of solutions to electric power supply chains with power generators, suppliers, transmission service providers, and the inclusion of consumer demands. In particular, I advocate the application of finite-dimensional variational inequality theory, projected dynamical systems theory, game theory, network theory, and other tools that have been recently proposed for the modeling and analysis of supply chain networks (cf. Nagurney (2006)) to electric power markets. This dissertation contributes to the extant literature on the modeling, analysis, and solution of supply chain networks, including global supply chains, in general, and electric power supply chains, in particular, in the following ways. It develops a theoretical framework for modeling, pricing analysis, and computation of electric power flows/transactions in electric power systems using the rationale for supply chain analysis. The models developed include both static and dynamic ones. The dissertation also adds a new dimension to the methodology of the theory of projected dynamical systems by proving that, irrespective of the speeds of adjustment, the equilibrium of the system remains the same. Finally, I include alternative fuel suppliers, along with their behavior into the supply chain modeling and analysis framework. This dissertation has strong practical implications. In an era in which technology and globalization, coupled with increasing risk and uncertainty, complicate electricity demand and supply within and between nations, the successful management of electric power systems and pricing become increasingly pressing topics with relevance not only for economic prosperity but also national security. This dissertation addresses such related topics by providing models, pricing tools, and algorithms for decentralized electric power supply chains. This dissertation is based heavily on the following

Pinning Synchronization of Linear Complex Coupling Synchronous Generators Network of Hydroelectric Generating Set

Directory of Open Access Journals (Sweden)

Xuefei Wu

2014-01-01

Full Text Available A novel linear complex system for hydroturbine-generator sets in multimachine power systems is suggested in this paper and synchronization of the power-grid networks is studied. The advanced graph theory and stability theory are combined to solve the problem. Here we derive a sufficient condition under which the synchronous state of power-grid networks is stable in disturbance attenuation. Finally, numerical simulations are provided to illustrate the effectiveness of the results by the IEEE 39 bus system.

Comparisons between designs for single-sided linear electric motors: Homopolar synchronous and induction

Science.gov (United States)

Nondahl, T. A.; Richter, E.

1980-09-01

A design study of two types of single sided (with a passive rail) linear electric machine designs, namely homopolar linear synchronous machines (LSM's) and linear induction machines (LIM's), is described. It is assumed the machines provide tractive effort for several types of light rail vehicles and locomotives. These vehicles are wheel supported and require tractive powers ranging from 200 kW to 3735 kW and top speeds ranging from 112 km/hr to 400 km/hr. All designs are made according to specified magnetic and thermal criteria. The LSM advantages are a higher power factor, much greater restoring forces for track misalignments, and less track heating. The LIM advantages are no need to synchronize the excitation frequency precisely to vehicle speed, simpler machine construction, and a more easily anchored track structure. The relative weights of the two machine types vary with excitation frequency and speed; low frequencies and low speeds favor the LSM.

The need to strengthen the electricity market with a better network

International Nuclear Information System (INIS)

Morch, Stein

2002-01-01

Price regions in the Nordic electricity market has been common. The competition authorities, the electricity exchange and the other actors indicate the need for increased capacity of the network. A larger concentration of big, competing producers in the Nordic countries is not desirable. The current competition among bidders is satisfactory at a common price level. Under conditions of price regions it is essential that there is competition in the area, which has not always been the case

Compressive stress-electrical conductivity characteristics of multiwall carbon nanotube networks

Czech Academy of Sciences Publication Activity Database

Slobodian, P.; Říha, Pavel; Lengálová, A.; Sáha, P.

2011-01-01

Roč. 46, č. 9 (2011), s. 3186-3190 ISSN 0022-2461 R&D Projects: GA AV ČR IAA200600803 Institutional research plan: CEZ:AV0Z20600510 Keywords : carbon nanotube network * compression * electrical conductivity * stress sensor Subject RIV: BK - Fluid Dynamics Impact factor: 2.015, year: 2011

Assembling networks of microbial genomes using linear programming.

Science.gov (United States)

Holloway, Catherine; Beiko, Robert G

2010-11-20

Microbial genomes exhibit complex sets of genetic affinities due to lateral genetic transfer. Assessing the relative contributions of parent-to-offspring inheritance and gene sharing is a vital step in understanding the evolutionary origins and modern-day function of an organism, but recovering and showing these relationships is a challenging problem. We have developed a new approach that uses linear programming to find between-genome relationships, by treating tables of genetic affinities (here, represented by transformed BLAST e-values) as an optimization problem. Validation trials on simulated data demonstrate the effectiveness of the approach in recovering and representing vertical and lateral relationships among genomes. Application of the technique to a set comprising Aquifex aeolicus and 75 other thermophiles showed an important role for large genomes as 'hubs' in the gene sharing network, and suggested that genes are preferentially shared between organisms with similar optimal growth temperatures. We were also able to discover distinct and common genetic contributors to each sequenced representative of genus Pseudomonas. The linear programming approach we have developed can serve as an effective inference tool in its own right, and can be an efficient first step in a more-intensive phylogenomic analysis.

Micro-grid platform based on NODE.JS architecture, implemented in electrical network instrumentation

Science.gov (United States)

Duque, M.; Cando, E.; Aguinaga, A.; Llulluna, F.; Jara, N.; Moreno, T.

2016-05-01

In this document, I propose a theory about the impact of systems based on microgrids in non-industrialized countries that have the goal to improve energy exploitation through alternatives methods of a clean and renewable energy generation and the creation of the app to manage the behavior of the micro-grids based on the NodeJS, Django and IOJS technologies. The micro-grids allow the optimal way to manage energy flow by electric injection directly in electric network small urban's cells in a low cost and available way. In difference from conventional systems, micro-grids can communicate between them to carry energy to places that have higher demand in accurate moments. This system does not require energy storage, so, costs are lower than conventional systems like fuel cells, solar panels or else; even though micro-grids are independent systems, they are not isolated. The impact that this analysis will generate, is the improvement of the electrical network without having greater control than an intelligent network (SMART-GRID); this leads to move to a 20% increase in energy use in a specified network; that suggest there are others sources of energy generation; but for today's needs, we need to standardize methods and remain in place to support all future technologies and the best option are the Smart Grids and Micro-Grids.

Impact of Social Network and Business Model on Innovation Diffusion of Electric Vehicles in China

Directory of Open Access Journals (Sweden)

D. Y. Kong

2014-01-01

Full Text Available The diffusion of electric vehicles (EVs involves not only the technological development but also the construction of complex social networks. This paper uses the theory of network control to analyze the influence of network forms on EV diffusion in China, especially focusing on the building of EV business models (BMs and the resulting effects and control on the diffusion of EVs. The Bass model is adopted to forecast the diffusion process of EVs and genetic algorithm is used to estimate the parameters based on the diffusion data of Hybrid Electric Vehicle (HEV in the United States and Japan. Two different social network forms and BMs are selected, that is, battery leasing model and vehicle purchasing model, to analyze how different network forms may influence the innovation coefficient and imitation coefficient in the Bass model, which will in turn result in different diffusion results. Thereby, we can find the appropriate network forms and BMs for EVs which is suitable to the local market conditions.

Simulación de sistemas eléctricos con cargas no lineales y variantes en el tiempo Simulation of electric systems with non-linear and time-variant loads

Directory of Open Access Journals (Sweden)

William Carvajal Carreño

2011-06-01

Full Text Available En este artículo se propone una metodología, implementada en un programa de computador por medio de una interfase gráfica con el usuario (GUI, que permite simular redes eléctricas con elementos no lineales (principalmente convertidores de potencia, tanto en el dominio del tiempo como en el dominio de la frecuencia. El método propuesto combina dos tipos de análisis en una herramienta flexible, de forma que se puedan aprovechar las ventajas de cada uno de ellos, y que permita analizar sistemas lineales y no lineales en el dominio del tiempo, sistemas lineales en el dominio de la frecuencia y sistemas mixtos utilizando este método híbrido. La herramienta es un instrumento de cálculo versátil y completo que puede ser usado de una manera sencilla, para los estudios de análisis de propagación de armónicos en las redes eléctricas. Se presentan ejemplos extraídos de la bibliografía utilizada en cursos de sistemas de potencia, circuitos eléctricos y electrónica de potencia, así como sistemas de prueba para análisis de cargas no lineales o variantes en el tiempo, generados como marco de referencia para las simulaciones de armónicos. Se analizan los resultados y se hacen observaciones acerca de las características de convergencia y estabilidad algorítmica del método propuesto. Dada la complejidad y carga computacional del análisis del método híbrido propuesto, se han seleccionado modelos de elementos, modelos de los componentes del sistema y algoritmos apropiados, para que la solución del problema se realice dentro de límites razonables de tiempo de simulación y consumo de recursos del equipo de cómputo.This paper deals with a methodology implemented in a computer program with a Graphic User Interface (GUI that allows us to simulate electrical networks (mainly power converters with non linear elements. The proposed approach mixes two analysis types in a flexible tool, in that way, we can take advantage of the features of each

Artificial neural networks applied to the prediction of spot prices in the market of electric energy

International Nuclear Information System (INIS)

Rodrigues, Alcantaro Lemes; Grimoni, Jose Aquiles Baesso

2010-01-01

The commercialization of electricity in Brazil as well as in the world has undergone several changes over the past 20 years. In order to achieve an economic balance between supply and demand of the good called electricity, stakeholders in this market follow both rules set by society (government, companies and consumers) and set by the laws of nature (hydrology). To deal with such complex issues, various studies have been conducted in the area of computational heuristics. This work aims to develop a software to forecast spot market prices in using artificial neural networks (ANN). ANNs are widely used in various applications especially in computational heuristics, where non-linear systems have computational challenges difficult to overcome because of the effect named 'curse of dimensionality'. This effect is due to the fact that the current computational power is not enough to handle problems with such a high combination of variables. The challenge of forecasting prices depends on factors such as: (a) foresee the demand evolution (electric load); (b) the forecast of supply (reservoirs, hydrology and climate), capacity factor; and (c) the balance of the economy (pricing, auctions, foreign markets influence, economic policy, government budget and government policy). These factors are considered be used in the forecasting model for spot market prices and the results of its effectiveness are tested and huge presented. (author)

Linearity and Non-linearity of Photorefractive effect in Materials ...

African Journals Online (AJOL)

In this paper we have studied the Linearity and Non-linearity of Photorefractive effect in materials using the band transport model. For low light beam intensities the change in the refractive index is proportional to the electric field for linear optics while for non- linear optics the change in refractive index is directly proportional ...

Electricity price forecasting using Enhanced Probability Neural Network

International Nuclear Information System (INIS)

Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

2010-01-01

This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the ''spikes'' can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment. (author)

Random Linear Network Coding for 5G Mobile Video Delivery

Directory of Open Access Journals (Sweden)

Dejan Vukobratovic

2018-03-01

Full Text Available An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G 3GPP New Radio (NR standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC. In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.

Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks

International Nuclear Information System (INIS)

Sigala, R; Smerieri, A; Camorani, P; Schüz, A; Erokhin, V

2013-01-01

Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments. (paper)

Future vision of advanced telecommunication networks for electric utilities; Denki jigyo ni okeru joho tsushin network no shorai vision

Energy Technology Data Exchange (ETDEWEB)

Tonaru, S.; Ono, K.; Sakai, S.; Kawai, Y.; Tsuboi, A. [Central Research Institute of Electric Power Industry, Tokyo (Japan); Manabe, S. [Shikoku Electric Power Co., Inc., Kagawa (Japan); Miki, Y. [Kansai Electric Power Co. Inc., Osaka (Japan)

1995-06-01

The vision of an advanced information system is proposed to cope with the future social demand and business environmental change in electric utilities. At the large turning point such as drastic reconsideration of Electricity Utilities Industry Law, further improvement of efficiency and cost reduction are requested as well as business innovation such as proposal of a new business policy. For that purpose utilization of information and its technology is indispensable, and use of multimedia and common information in organization are the future direction for improving information basis. Consequently, free information networks without any limitation due to person and media are necessary, and the following are important: high-speed, high-frequency band, digital, easily connectable and multimedia transmission lines, and cost reduction and high reliability of networks. Based on innovation of information networks and the clear principle on advanced information system, development of new applications by multimedia technologies, diffusion of communication terminals, and promotion of standardization are essential. 60 refs., 30 figs., 5 tabs.

A Design Of Feeding Network For A Dual-Linear Polarization, Stacked, Probe-Fed Microstrip Patch Antenna Array

DEFF Research Database (Denmark)

Jaworski, G.; Krozer, Viktor

2004-01-01

Components of multilayer feed network are presented for application in broad-band dual-linear polarized stacked C-band antenna. Measurement results of wide band matching circuits and different types of power divider networks constituting parts of BFN demonstrate wideband operation. Suitable...

Linear summation of outputs in a balanced network model of motor cortex.

Science.gov (United States)

Capaday, Charles; van Vreeswijk, Carl

2015-01-01

Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.

Modelling renewable energy resource and the electricity network (East Midlands region)

Energy Technology Data Exchange (ETDEWEB)

Newton, P.A.; Ma, T.

2002-07-01

The UK Government's targets for renewable generation and combined heat and power (CHP) are expected to result in a significant growth in embedded generation. This report describes the results of a study of the capability of the electricity distribution network in the East Midlands to accept embedded generation. Detailed network studies were performed for two sample networks: one representing an urban network (Leicester) and one representing a rural network (Boston). The 132 kV networks of the grid groups covering these areas were also studied. This included an examination of the connection points from major 132 kV busbars at grid supply points down to 11 kV primary substations. Power system studies were performed to identify the constraints and capabilities of the existing network, These studies included load flow to examine voltage profile and overloading, fault level analysis and transient studies to examine generator and network stability following faults on the network and voltage step change due to generator tripping. Space network capacity for the region was identified and used to assess the ability to accommodate regional targets for renewables and CHP. The study also examined constraining factors and potential solutions, including four improvement scenarios.

Impact of Electric Vehicle Charging Station Load on Distribution Network

Directory of Open Access Journals (Sweden)

Sanchari Deb

2018-01-01

Full Text Available Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP index. The results obtained indicate the efficacy of the VRP index.

An Optimal Domestic Electric Vehicle Charging Strategy for Reducing Network Transmission Loss While Taking Seasonal Factors into Consideration

Directory of Open Access Journals (Sweden)

Yuancheng Zhao

2018-01-01

Full Text Available With the rapid growth of domestic electric vehicle charging loads, the peak-valley gap and power fluctuation rate of power systems increase sharply, which can lead to the increase of network losses and energy efficiency reduction. This paper tries to regulate network loads and reduce power system transmission loss by optimizing domestic electric vehicle charging loads. In this paper, a domestic electric vehicle charging loads model is first developed by analyzing the key factors that can affect users’ charging behavior. Subsequently, the Monte Carlo method is proposed to simulate the power consumption of a cluster of domestic electric vehicles. After that, an optimal electric vehicle charging strategy based on the 0-1 integer programming is presented to regulate network daily loads. Finally, by taking the IEEE33 distributed power system as an example, this paper tries to verify the efficacy of the proposed optimal charging strategy and the necessity for considering seasonal factors when scheduling electric vehicle charging loads. Simulation results show that the proposed 0-1 integer programming method does have good performance in reducing the network peak-valley gap, voltage fluctuation rate, and transmission loss. Moreover, it has some potential to further reduce power system transmission loss when seasonal factors are considered.

Spare part management of an electricity distribution network

Energy Technology Data Exchange (ETDEWEB)

Lauronen, J.

1998-07-01

Electricity distribution companies are required to improve their operational cost effectiveness. The storage systems of the companies have traditionally been based on the 'adequate' number of stores with plenty of different components. Therefore, they are potential objects for cost reduction. The effective operation of spare part management of an electricity distribution network requires that the spare components can be delivered at the fault site quickly in order to avoid excessive outage costs. In a fault situation the stores form a net structure. Currently the rural electricity distribution companies lack suitable methods for designing a spare part storage system. This thesis presents a suitable method for the designing problem. The models assume that faults of a distribution network are stochastic. Therefore, they are best suited for component types installed in large quantities. Improved methods for defining the outage, material and total costs for perpetual order quantity and periodic order-up-to-level storage control systems are described. The method for determining the control parameters of the stores is also presented and ways for finding the necessary initial parameter values are introduced. The developed method is tested in Haemeen Saehko Oy (HSOY). The results of the calculations are given. The key findings are: Small differences in the designing results can increase costs remarkably. For example, in HSOY too low stock levels can result in even eight folds higher outage costs than in the proper design. The best number of stores is not the same for all component types. For example, in HSOY the best number of stores is seven for the 50 kVA transformers and one for the 315 kVA transformers in a summer. If the stock levels are increased the protection against the demand variations is the better the shorter the duration of the review period and/or the replenishment lead time is. (orig.)

Spare part management of an electricity distribution network

Energy Technology Data Exchange (ETDEWEB)

Lauronen, J

1998-07-01

Electricity distribution companies are required to improve their operational cost effectiveness. The storage systems of the companies have traditionally been based on the 'adequate' number of stores with plenty of different components. Therefore, they are potential objects for cost reduction. The effective operation of spare part management of an electricity distribution network requires that the spare components can be delivered at the fault site quickly in order to avoid excessive outage costs. In a fault situation the stores form a net structure. Currently the rural electricity distribution companies lack suitable methods for designing a spare part storage system. This thesis presents a suitable method for the designing problem. The models assume that faults of a distribution network are stochastic. Therefore, they are best suited for component types installed in large quantities. Improved methods for defining the outage, material and total costs for perpetual order quantity and periodic order-up-to-level storage control systems are described. The method for determining the control parameters of the stores is also presented and ways for finding the necessary initial parameter values are introduced. The developed method is tested in Haemeen Saehko Oy (HSOY). The results of the calculations are given. The key findings are: Small differences in the designing results can increase costs remarkably. For example, in HSOY too low stock levels can result in even eight folds higher outage costs than in the proper design. The best number of stores is not the same for all component types. For example, in HSOY the best number of stores is seven for the 50 kVA transformers and one for the 315 kVA transformers in a summer. If the stock levels are increased the protection against the demand variations is the better the shorter the duration of the review period and/or the replenishment lead time is. (orig.)

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

Science.gov (United States)

Wu, Wei; Cui, Bao-Tong

2007-07-01

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.

PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

Directory of Open Access Journals (Sweden)

Yuanchang Zhong

2014-01-01

Full Text Available The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

Planning of optimal electricity and gas distribution networks; Planung von optimalen Strom- und Gasverteilungsnetzen

Energy Technology Data Exchange (ETDEWEB)

Berg, Andreas; Hinueber, Gerd; Moser, Albert [RWTH Aachen (DE). Inst. fuer Elektrische Anlagen und Energiewirtschaft (IAEW)

2009-12-15

Framework conditions for the planning of energy networks are changing under the influence of European energy policy, incentive regulation and the politically motivated promotion of new technologies such as electromobility or intelligent metering. The future use of information and communication technologies in energy networks will create new degrees of freedom. This will necessitate changes in the way in which gas and electricity networks have been planned in the past. Especially computer-assisted methods for objectifying planning decisions are moving into the focus of network operators as a valuable network development tool.

Discovery of Boolean metabolic networks: integer linear programming based approach.

Science.gov (United States)

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

Systems for protection and automation of distribution electric networks 22 kv

International Nuclear Information System (INIS)

Horak, M.

2012-01-01

This article deals with the new concept of fault location in overhead and cable 22 kV distribution systems, which are operated by ZSE Distribucia, a.s.. In the past the localisation of faults was a demanding and lengthy procedure, because the electric protection indicated only the affected output feeder. The exact fault point was then localised by manipulation in the field and by test switching in the power line where the fault took place. The current development of electric facilities and sharp fall in prices give use the possibility to broadly apply the simple digital metering devices equipped with the electric protection function. Once these devices are positioned densely along the individual power lines of the network, it is possible to localise the failure position quite exactly and electricity supply can be quickly restored. (Authors)

A multi-criteria decision aid methodology to design electric vehicles public charging networks

Directory of Open Access Journals (Sweden)

João Raposo

2015-05-01

Full Text Available This article presents a new multi-criteria decision aid methodology, dynamic-PROMETHEE, here used to design electric vehicle charging networks. In applying this methodology to a Portuguese city, results suggest that it is effective in designing electric vehicle charging networks, generating time and policy based scenarios, considering offer and demand and the city’s urban structure. Dynamic-PROMETHE adds to the already known PROMETHEE’s characteristics other useful features, such as decision memory over time, versatility and adaptability. The case study, used here to present the dynamic-PROMETHEE, served as inspiration and base to create this new methodology. It can be used to model different problems and scenarios that may present similar requirement characteristics.

A multi-criteria decision aid methodology to design electric vehicles public charging networks

Science.gov (United States)

Raposo, João; Rodrigues, Ana; Silva, Carlos; Dentinho, Tomaz

2015-05-01

This article presents a new multi-criteria decision aid methodology, dynamic-PROMETHEE, here used to design electric vehicle charging networks. In applying this methodology to a Portuguese city, results suggest that it is effective in designing electric vehicle charging networks, generating time and policy based scenarios, considering offer and demand and the city's urban structure. Dynamic-PROMETHE adds to the already known PROMETHEE's characteristics other useful features, such as decision memory over time, versatility and adaptability. The case study, used here to present the dynamic-PROMETHEE, served as inspiration and base to create this new methodology. It can be used to model different problems and scenarios that may present similar requirement characteristics.

Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

International Nuclear Information System (INIS)

Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

2015-01-01

Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

Science.gov (United States)

Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

2009-01-01

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

Pre-Trained Neural Networks used for Non-Linear State Estimation

DEFF Research Database (Denmark)

Bayramoglu, Enis; Andersen, Nils Axel; Ravn, Ole

2011-01-01

of the paramters in the distribution. This transformation is approximated by a neural network using offline training, which is based on monte carlo sampling. In the paper, there will also be presented a method to construct a flexible distributions well suited for covering the effect of the non-linearities......The paper focuses on nonlinear state estimation assuming non-Gaussian distributions of the states and the disturbances. The posterior distribution and the aposteriori distribution is described by a chosen family of paramtric distributions. The state transformation then results in a transformation...

Preparing tomorrow's network today: RTE at the crossroads of the European electricity system. Cross-border electricity interconnections Key issues and figures - 2014 Edition

International Nuclear Information System (INIS)

2014-10-01

The interconnected electricity transmission network is a key element for ensuring security of supply, the creation of a single market and the integration of renewable energies. RTE and its European partners provide strengthened coordination by the use of interconnections to ensure solidarity between European countries. Interconnections also allow an electricity supplier to sell its energy to a customer located in another country in Europe. They contribute on a European scale to optimising the use of production means and in particular the integration of variable renewable energies. RTE, within the EnTSo-E (European Network of Transmission System Operators for Electricity), contributes to the publication of a common vision of the future of networks by 2030. The needs for new interconnection capacity are identified in the ten-year European network development plan (TYnDP) on the basis of a cost-benefit analysis. France is interconnected to all its neighbours via many cross-border links. However interconnection capacity with the various countries and its use differ widely. The usage profile of the interconnections is specific to each border and varies according to: - the characteristics of the production mix of each country, and in particular the level of production of renewable energies. - the level of consumption, which depends on the season, the type of day (working or non-working) the time of day, etc. - import and export capacity, which may be different depending on the internal constraints of the networks of each country

Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks

OpenAIRE

Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido

2011-01-01

Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confi...

Forecasting electricity market pricing using artificial neural networks

International Nuclear Information System (INIS)

Pao, Hsiao-Tien

2007-01-01

Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long

Application of high-resolution domestic electricity load profiles in network modelling

DEFF Research Database (Denmark)

Marszal, Anna Joanna; Mendaza, Iker Diaz de Cerio; Heiselberg, Per Kvols

2016-01-01

the generated profiles are inputted in a low-voltage network model created in DIgSILENT PowerFactory. By means of employing 1 hour based demand and generation profiles in during dynamic studies, the representation of the local power system performance might sometimes not be as accurate as needed. In the test...... with modeling when 1-minute domestic electricity demand and generation profiles are used as inputs. The analysis is done with a case study of low-voltage network located in Northern Denmark. The analysis includes two parts. The first part focuses on modeling the domestic demands and on-site generation in 1......-minute resolution. The load profiles of the household appliances are created using a bottom-up model, which uses the 1-minute cycle power use characteristics of a single appliance as the main building block. The profiles of heavy electric appliances, such as heat pump, are not included in the above...

Optimization models and techniques for implementation and pricing of electricity markets

International Nuclear Information System (INIS)

Madrigal Martinez, M.

2001-01-01

The operation and planning of vertically integrated electric power systems can be optimized using models that simulate solutions to problems. As the electric power industry is going through a period of restructuring, there is a need for new optimization tools. This thesis describes the importance of optimization tools and presents techniques for implementing them. It also presents methods for pricing primary electricity markets. Three modeling groups are studied. The first considers a simplified continuous and discrete model for power pool auctions. The second considers the unit commitment problem, and the third makes use of a new type of linear network-constrained clearing system model for daily markets for power and spinning reserve. The newly proposed model considers bids for supply and demand and bilateral contracts. It is a direct current model for the transmission network

The Study on the Communication Network of Wide Area Measurement System in Electricity Grid

Science.gov (United States)

Xiaorong, Cheng; Ying, Wang; Yangdan, Ni

Wide area measurement system(WAMS) is a fundamental part of security defense in Smart Grid, and the communication system of WAMS is an important part of Electric power communication network. For a large regional network is concerned, the real-time data which is transferred in the communication network of WAMS will affect the safe operation of the power grid directly. Therefore, WAMS raised higher requirements for real-time, reliability and security to its communication network. In this paper, the architecture of WASM communication network was studied according to the seven layers model of the open systems interconnection(OSI), and the network architecture was researched from all levels. We explored the media of WAMS communication network, the network communication protocol and network technology. Finally, the delay of the network were analyzed.

IPv6-Based Smart Metering Network for Monitoring Building Electricity

Directory of Open Access Journals (Sweden)

Dong Xu

2013-01-01

Full Text Available A smart electricity monitoring system of building is presented using ZigBee and internet to establish the network. This system consists of three hardware layers: the host PC, the router, and the sensor nodes. A hierarchical ant colony algorithm is developed for data transmission among the wireless sensor nodes. The wireless communication protocol is also designed based on IPv6 protocol on IEEE 802.15.4 wireless network. All-IP approach and peer-to-peer mode are integrated to optimize the network building. Each node measures the power, current, and voltage and transmits them to the host PC through the router. The host software is designed for building test characteristics, having a tree hierarchy and a friendly interface for the user. The reliability and accuracy of this monitoring system are verified in the experiment and application.

Towards future electricity networks - Final report

Energy Technology Data Exchange (ETDEWEB)

Papaemmanouil, A.

2008-07-01

This comprehensive final report for the Swiss Federal Office of Energy (SFOE) reviews work done on the development of new power transmission planning tools for restructured power networks. These are needed in order to face the challenges that arise due to economic, environmental and social issues. The integration of transmission, generation and energy policy planning in order to support a common strategy with respect to sustainable electricity networks is discussed. In the first phase of the project the main focus was placed on the definition of criteria and inputs that are most likely to affect sustainable transmission expansion plans. Models, concepts, and methods developed in order to study the impact of the internalisation of external costs in power production are examined. To consider external costs in the planning process, a concurrent software tool has been implemented that is capable of studying possible development scenarios. The report examines a concept that has been developed to identify congested transmission lines or corridors and evaluates the dependencies between the various market participants. The paper includes a set of three appendices that include a paper on the 28{sup th} USAEE North American conference, an abstract from Powertech 2009 and an SFOE report from July 2008.

Towards future electricity networks - Final report

International Nuclear Information System (INIS)

Papaemmanouil, A.

2008-01-01

This comprehensive final report for the Swiss Federal Office of Energy (SFOE) reviews work done on the development of new power transmission planning tools for restructured power networks. These are needed in order to face the challenges that arise due to economic, environmental and social issues. The integration of transmission, generation and energy policy planning in order to support a common strategy with respect to sustainable electricity networks is discussed. In the first phase of the project the main focus was placed on the definition of criteria and inputs that are most likely to affect sustainable transmission expansion plans. Models, concepts, and methods developed in order to study the impact of the internalisation of external costs in power production are examined. To consider external costs in the planning process, a concurrent software tool has been implemented that is capable of studying possible development scenarios. The report examines a concept that has been developed to identify congested transmission lines or corridors and evaluates the dependencies between the various market participants. The paper includes a set of three appendices that include a paper on the 28 th USAEE North American conference, an abstract from Powertech 2009 and an SFOE report from July 2008.

Damped oscillations of linear systems a mathematical introduction

CERN Document Server

Veselić, Krešimir

2011-01-01

The theory of linear damped oscillations was originally developed more than hundred years ago and is still of vital research interest to engineers, mathematicians and physicists alike. This theory plays a central role in explaining the stability of mechanical structures in civil engineering, but it also has applications in other fields such as electrical network systems and quantum mechanics. This volume gives an introduction to linear finite dimensional damped systems as they are viewed by an applied mathematician. After a short overview of the physical principles leading to the linear system model, a largely self-contained mathematical theory for this model is presented. This includes the geometry of the underlying indefinite metric space, spectral theory of J-symmetric matrices and the associated quadratic eigenvalue problem. Particular attention is paid to the sensitivity issues which influence numerical computations. Finally, several recent research developments are included, e.g. Lyapunov stability and ...

Price-based optimal control of power flow in electrical energy transmission networks

NARCIS (Netherlands)

Jokic, A.; Lazar, M.; Bosch, van den P.P.J.; Bemporad, A.; Bicchi, A.; Buttazzo, G.

2007-01-01

This article presents a novel control scheme for achieving optimal power balancing and congestion control in electrical energy transmission networks via nodal prices. We develop an explicit controller that guarantees economically optimal steady-state operation while respecting all line flow

A control technique for integration of DG units to the electrical networks

DEFF Research Database (Denmark)

Pouresmaeil, Edris; Miguel-Espinar, Carlos; Massot-Campos, Miquel

2013-01-01

This paper deals with a multiobjective control technique for integration of distributed generation (DG) resources to the electrical power network. The proposed strategy provides compensation for active, reactive, and harmonic load current components during connection of DG link to the grid...

A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

Science.gov (United States)

Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

2018-02-01

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

Directory of Open Access Journals (Sweden)

Xiaobo Nie

2016-01-01

Full Text Available Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.

Effects of extracellular potassium diffusion on electrically coupled neuron networks

Science.gov (United States)

Wu, Xing-Xing; Shuai, Jianwei

2015-02-01

Potassium accumulation and diffusion during neuronal epileptiform activity have been observed experimentally, and potassium lateral diffusion has been suggested to play an important role in nonsynaptic neuron networks. We adopt a hippocampal CA1 pyramidal neuron network in a zero-calcium condition to better understand the influence of extracellular potassium dynamics on the stimulus-induced activity. The potassium concentration in the interstitial space for each neuron is regulated by potassium currents, Na+-K+ pumps, glial buffering, and ion diffusion. In addition to potassium diffusion, nearby neurons are also coupled through gap junctions. Our results reveal that the latency of the first spike responding to stimulus monotonically decreases with increasing gap-junction conductance but is insensitive to potassium diffusive coupling. The duration of network oscillations shows a bell-like shape with increasing potassium diffusive coupling at weak gap-junction coupling. For modest electrical coupling, there is an optimal K+ diffusion strength, at which the flow of potassium ions among the network neurons appropriately modulates interstitial potassium concentrations in a degree that provides the most favorable environment for the generation and continuance of the action potential waves in the network.

Scenarios of power transmission networks; Cenarios de redes de transmissao de energia eletrica

Energy Technology Data Exchange (ETDEWEB)

Souza Fonseca, L.G. de; Savi, T C.O.; Morozowski Filho, M; Camargo, C C [ELETROSUL, Florianopolis, SC (Brazil)

1985-12-31

This work discusses the electrical network expansion considering long term horizons and a purpose of methodology for the establishment of transmission network scenarios. As part of the transmission scenario studies the network expansion problem is described by linearized power flow models and, for transmission system analysis and synthesis, the minimum effort criteria and interactive SINTRA program are used. 4 refs., 3 figs., 2 tabs.

Neural network control of a parallel hybrid-electric propulsion system for a small unmanned aerial vehicle

Science.gov (United States)

Harmon, Frederick G.

2005-11-01

Parallel hybrid-electric propulsion systems would be beneficial for small unmanned aerial vehicles (UAVs) used for military, homeland security, and disaster-monitoring missions. The benefits, due to the hybrid and electric-only modes, include increased time-on-station and greater range as compared to electric-powered UAVs and stealth modes not available with gasoline-powered UAVs. This dissertation contributes to the research fields of small unmanned aerial vehicles, hybrid-electric propulsion system control, and intelligent control. A conceptual design of a small UAV with a parallel hybrid-electric propulsion system is provided. The UAV is intended for intelligence, surveillance, and reconnaissance (ISR) missions. A conceptual design reveals the trade-offs that must be considered to take advantage of the hybrid-electric propulsion system. The resulting hybrid-electric propulsion system is a two-point design that includes an engine primarily sized for cruise speed and an electric motor and battery pack that are primarily sized for a slower endurance speed. The electric motor provides additional power for take-off, climbing, and acceleration and also serves as a generator during charge-sustaining operation or regeneration. The intelligent control of the hybrid-electric propulsion system is based on an instantaneous optimization algorithm that generates a hyper-plane from the nonlinear efficiency maps for the internal combustion engine, electric motor, and lithium-ion battery pack. The hyper-plane incorporates charge-depletion and charge-sustaining strategies. The optimization algorithm is flexible and allows the operator/user to assign relative importance between the use of gasoline, electricity, and recharging depending on the intended mission. A MATLAB/Simulink model was developed to test the control algorithms. The Cerebellar Model Arithmetic Computer (CMAC) associative memory neural network is applied to the control of the UAVs parallel hybrid-electric

Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.

Science.gov (United States)

Knapp, Bettina; Kaderali, Lars

2013-01-01

Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4(+) T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Low power RF measurements of travelling wave type linear accelerator

International Nuclear Information System (INIS)

Reddy, Sivananda; Wanmode, Yashwant; Bhisikar, A.; Shrivastava, Purushottam

2015-01-01

RRCAT is engaged in the development of travelling wave (TW) type linear accelerator for irradiation of industrial and agricultural products. TW accelerator designed for 2π/3 mode to operate at frequency of 2856 MHz. It consists of input coupler, buncher cells, regular cells and output coupler. Low power measurement of this structure includes measurement of resonant frequency of the cells for different resonant modes and quality factor, tuning of input-output coupler and measurement of phase advance per cell and electric field in the structure. Steele's non-resonant perturbation technique has been used for measurement of phase advance per cell and electric field in the structure. Kyhl's method has been used for the tuning of input-output coupler. Computer based automated bead pull set-up has been developed for measurement of phase advance per cell and electric field profile in the structure. All the codes are written in Python for interfacing of Vector Network Analyzer (VNA) , stepper motor with computer. These codes also automate the measurement process. This paper describes the test set- up for measurement and results of measurement of travelling wave type linear accelerating structure. (author)

Econometric model as a regulatory tool in electricity distribution - Case Network Performance Assessment Model

International Nuclear Information System (INIS)

Honkapuro, S.; Lassila, J.; Viljainen, S.; Tahvanainen, K.; Partanen, J.

2004-01-01

Electricity distribution companies operate in the state of natural monopolies since building of parallel networks is not cost-effective. Monopoly companies do not have pressure from the open markets to keep their prices and costs at reasonable level. The regulation of these companies is needed to prevent the misuse of the monopoly position. Regulation is usually focused either on the profit of company or on the price of electricity. In this document, the usability of an econometric model in the regulation of electricity distribution companies is evaluated. Regulation method which determines allowed income for each company with generic computation model can be seen as an econometric model. As the special case of an econometric model, the method called Network Performance Assessment Model, NPAM (Naetnyttomodellen in Swedish), is analysed. NPAM is developed by Swedish Energy Agency (STEM) for the regulation of electricity distribution companies. Both theoretical analysis and calculations of an example network area are presented in this document to find the major directing effects of the model. The parameters of NPAM, which are used in the calculations of this research report, were dated on 30th of March 2004. These parameters were most recent available at the time when analysis was done. However, since NPAM is under development, the parameters have been constantly changing. Therefore slightly changes in the results can occur if calculations were made with latest parameters. However, main conclusions are same and do not depend on exact parameters. (orig.)

Benefits analysis of Soft Open Points for electrical distribution network operation

International Nuclear Information System (INIS)

Cao, Wanyu; Wu, Jianzhong; Jenkins, Nick; Wang, Chengshan; Green, Timothy

2016-01-01

Highlights: • An analysis framework was developed to quantify the operational benefits. • The framework considers both network reconfiguration and SOP control. • Benefits were analyzed through both quantitative and sensitivity analysis. - Abstract: Soft Open Points (SOPs) are power electronic devices installed in place of normally-open points in electrical power distribution networks. They are able to provide active power flow control, reactive power compensation and voltage regulation under normal network operating conditions, as well as fast fault isolation and supply restoration under abnormal conditions. A steady state analysis framework was developed to quantify the operational benefits of a distribution network with SOPs under normal network operating conditions. A generic power injection model was developed and used to determine the optimal SOP operation using an improved Powell’s Direct Set method. Physical limits and power losses of the SOP device (based on back to back voltage-source converters) were considered in the model. Distribution network reconfiguration algorithms, with and without SOPs, were developed and used to identify the benefits of using SOPs. Test results on a 33-bus distribution network compared the benefits of using SOPs, traditional network reconfiguration and the combination of both. The results showed that using only one SOP achieved a similar improvement in network operation compared to the case of using network reconfiguration with all branches equipped with remotely controlled switches. A combination of SOP control and network reconfiguration provided the optimal network operation.

A recurrent neural network for solving bilevel linear programming problem.

Science.gov (United States)

He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian

2014-04-01

In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.

The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook

International Nuclear Information System (INIS)

Green, Robert C. II.; Wang, Lingfeng; Alam, Mansoor

2011-01-01

Plug-in hybrid electric vehicles (PHEVs) are the next big thing in the electric transportation market. While much work has been done to detail what economic costs and benefits PHEVs will have on consumers and producers alike, it seems that it is also important to understand what impact PHEVs will have on distribution networks nationwide. This paper finds that the impact of PHEVs on the distribution network can be determined using the following aspects of PHEVs: driving patterns, charging characteristics, charge timing, and vehicle penetration. The impacts that these aspects of PHEVs will have on distribution networks have been measured and calculated by multiple authors in different locations using many different tools that range from analytical techniques to simulations and beyond. While much work has already been completed in this area, there is still much to do. Areas left for improvement and future work will include adding more stochasticity into models as well as computing and analyzing reliability indices with respect to distribution networks. (author)

Implementation and Performance Evaluation of Distributed Cloud Storage Solutions using Random Linear Network Coding

DEFF Research Database (Denmark)

Fitzek, Frank; Toth, Tamas; Szabados, Áron

2014-01-01

This paper advocates the use of random linear network coding for storage in distributed clouds in order to reduce storage and traffic costs in dynamic settings, i.e. when adding and removing numerous storage devices/clouds on-the-fly and when the number of reachable clouds is limited. We introduce...... various network coding approaches that trade-off reliability, storage and traffic costs, and system complexity relying on probabilistic recoding for cloud regeneration. We compare these approaches with other approaches based on data replication and Reed-Solomon codes. A simulator has been developed...... to carry out a thorough performance evaluation of the various approaches when relying on different system settings, e.g., finite fields, and network/storage conditions, e.g., storage space used per cloud, limited network use, and limited recoding capabilities. In contrast to standard coding approaches, our...

Stable Operation and Electricity Generating Characteristics of a Single-Cylinder Free Piston Engine Linear Generator: Simulation and Experiments

Directory of Open Access Journals (Sweden)

Huihua Feng

2015-01-01

Full Text Available We present a novel design of a single-cylinder free piston engine linear generator (FPELG incorporating a linear motor as a rebound device. A systematic simulation model of this FPELG system was built containing a kinematic and dynamic model of the piston and mover, a magneto-electric model of the linear generator, a thermodynamic model of the single-cylinder engine, and a friction model between the piston ring and cylinder liner. Simulations were performed to understand the relationships between pre-set motor parameters and the running performance of the FPELG. From the simulation results, it was found that a motor rebound force with a parabolic profile had clear advantages over a force with a triangular profile, such as a higher running frequency and peak cylinder pressure, faster piston motion, etc. The rebound position and the amplitude of rebound force were also determined by simulations. The energy conversion characteristics of the generator were obtained from our FPELG test rig. The parameters of intake pressure, motor frequency, and load resistance were varied over certain ranges, and relationships among these three parameters were obtained. The electricity-generating characteristic parameters include output power and system efficiency, which can measure the quality of matching the controllable parameters. The output power can reach 25.9 W and the system efficiency can reach 13.7%. The results in terms of matching parameters and electricity-generating characteristics should be useful to future research in adapting these engines to various operating modes.

Design of RFID Mesh Network for Electric Vehicle Smart Charging Infrastructure

Energy Technology Data Exchange (ETDEWEB)

Chung, Ching-Yen; Shepelev, Aleksey; Qiu, Charlie; Chu, Chi-Cheng; Gadh, Rajit

2013-09-04

With an increased number of Electric Vehicles (EVs) on the roads, charging infrastructure is gaining an ever-more important role in simultaneously meeting the needs of the local distribution grid and of EV users. This paper proposes a mesh network RFID system for user identification and charging authorization as part of a smart charging infrastructure providing charge monitoring and control. The Zigbee-based mesh network RFID provides a cost-efficient solution to identify and authorize vehicles for charging and would allow EV charging to be conducted effectively while observing grid constraints and meeting the needs of EV drivers

Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.

Science.gov (United States)

Narimani, Zahra; Beigy, Hamid; Ahmad, Ashar; Masoudi-Nejad, Ali; Fröhlich, Holger

2017-01-01

Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.

Linear magnetic motor/generator. [to generate electric energy using magnetic flux for spacecraft power supply

Science.gov (United States)

Studer, P. A. (Inventor)

1982-01-01

A linear magnetic motor/generator is disclosed which uses magnetic flux to provide mechanical motion or electrical energy. The linear magnetic motor/generator includes an axially movable actuator mechanism. A permament magnet mechanism defines a first magnetic flux path which passes through a first end portion of the actuator mechanism. Another permament magnet mechanism defines a second magnetic flux path which passes through a second end portion of the actuator mechanism. A drive coil defines a third magnetic flux path passing through a third central portion of the actuator mechanism. A drive coil selectively adds magnetic flux to and subtracts magnetic flux from magnetic flux flowing in the first and second magnetic flux path.

Identifying the nonlinear mechanical behaviour of micro-speakers from their quasi-linear electrical response

Science.gov (United States)

Zilletti, Michele; Marker, Arthur; Elliott, Stephen John; Holland, Keith

2017-05-01

In this study model identification of the nonlinear dynamics of a micro-speaker is carried out by purely electrical measurements, avoiding any explicit vibration measurements. It is shown that a dynamic model of the micro-speaker, which takes into account the nonlinear damping characteristic of the device, can be identified by measuring the response between the voltage input and the current flowing into the coil. An analytical formulation of the quasi-linear model of the micro-speaker is first derived and an optimisation method is then used to identify a polynomial function which describes the mechanical damping behaviour of the micro-speaker. The analytical results of the quasi-linear model are compared with numerical results. This study potentially opens up the possibility of efficiently implementing nonlinear echo cancellers.

Competition and network expansion in the electricity market: an analysis of producers' strategic behavior

International Nuclear Information System (INIS)

Fumagalli, Elena; Garrone, Paola; Internullo, Marco

2006-01-01

Expansion of the transmission capacity is ODe of the most efficient means of enhancing competition in electricity markets. The issue is extremely relevant far the Italian electricity market, where competition in generation has not Jet been achieved. In order to study the effects on competition of a network expansion project, a description of the influence of transmission constraints on the strategic behavior of generators is necessary. The problem was addressed in the literature far a limited number only of simplified models. This work presents an original methodology (MIXEL), based on non-cooperative game theory, far the study of a rather broad set of electricity market models. The case study illustrated in this article, shows that the effects on competition of an expansion of the network is not always positive (or as positive) as expected, given the cases illustrated in the literature. The effects on competition vary with the market structure, the ratio between demand and supply and, above all, the size of the transmission capacity expansion. For these reasons, policy provisions mandating or encouraging expansion of the transmission system with the objective of promoting competition, should take into careful consideration the underlying market structure; in a similar way, provision encouraging divestiture of generation capacity should take into account the effects of the network [it

Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

Directory of Open Access Journals (Sweden)

Diogo Santos

2016-06-01

Full Text Available Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO - Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models - particularly for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. The work starts by describing the methodological approach followed during research and development, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this work reviews some related work to the subject.

Learning Bayesian network structure: towards the essential graph by integer linear programming tools

Czech Academy of Sciences Publication Activity Database

Studený, Milan; Haws, D.

2014-01-01

Roč. 55, č. 4 (2014), s. 1043-1071 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * integer linear programming * characteristic imset * essential graph Subject RIV: BA - General Mathematics Impact factor: 2.451, year: 2014 http://library.utia.cas.cz/separaty/2014/MTR/studeny-0427002.pdf

Competition and regulation in the European network industries. From general case to the case of electricity

International Nuclear Information System (INIS)

Jonquieres, F.

1995-01-01

The paper focuses on the institutional arrangements present situation in the European Electricity Supply Industry, which is characterized by its diversity. There is unquestionably, a trend to put pressure on the national electricity systems by the European Union organisms to accept the unbundling, Third Party Access to the network, deregulation etc. An opposing reaction appears, trying to underline the potential important drawbacks of such a trend. The conclusion of the author can be summarised as follows: Competition at the generation level? Yes[ Access to the network ? No[ (author)

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

Science.gov (United States)

Yousif, Adil; Elfaki, Faiz

2017-12-01

The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

The value of electricity distribution networks

International Nuclear Information System (INIS)

De Paoli, L.

2000-01-01

This article presents the results of a study aimed at evaluating parts of the distribution network of ENEL, in charge of distributing and supplying electricity to its captive market, that could be sold as a separate entity. To determine the asset value of these hypothetical companies, the discounted cash flow method has been used applied to the 147 ENEL's distributing zones. The econometric analysis shows that the relevant variables are the quantity sold to non residential and non big industrial consumers and the length of medium voltage lines. According to the available data and to the methodology chosen, the per client value of the distribution zones of ENEL varies substantially. The maximum value is bout three times the mean value and the minimum value is largely negative. The article maintains that changes in regulation could greatly modify the asset value of distribution networks. The main regulatory risks are linked to the degree of market opening, the introduction of compensation mechanisms between different distributors and the allowed maximum revenue fixed by energy Authority for a given period of time. This point is developed in the appendix where it is shown that the price cap method is decided on the basis of a rate of return which is valid at the moment of the cap fixing but that could be no longer valid if the rate of inflation varies [it

Identification of lines of electric lines of three-phase distribution networks in the composition of ASMAE

Directory of Open Access Journals (Sweden)

Omorov Turatbek

2017-01-01

Full Text Available The problem of protection of a three-phase four-wire distribution network (DEN with a voltage of 0.4 kV from the interruption of electric power lines is considered. On the basis of the analysis of the values of complex resistances of interpersonal sections of the main line of the network, criteria are proposed that allow detecting critical situations associated with discontinuities in power transmission lines. Using these criteria makes it possible to localize the places of breaks of phase and neutral wires. Mathematical models and methods that are used to evaluate the complex resistances of interpersonal sections of a three-phase network are briefly considered. The obtained results are oriented for use in the automated system of electricity control and accounting (ASMAE.

Influence of defects on the effective electrical conductivity of a monolayer produced by random sequential adsorption of linear k-mers onto a square lattice

Science.gov (United States)

Tarasevich, Yuri Yu.; Laptev, Valeri V.; Goltseva, Valeria A.; Lebovka, Nikolai I.

2017-07-01

The effect of defects on the behaviour of electrical conductivity, σ, in a monolayer produced by the random sequential adsorption of linear k-mers (particles occupying k adjacent sites) onto a square lattice is studied by means of a Monte Carlo simulation. The k-mers are deposited on the substrate until a jamming state is reached. The presence of defects in the lattice (impurities) and of defects in the k-mers with concentrations of dl and dk, respectively, is assumed. The defects in the lattice are distributed randomly before deposition and these lattice sites are forbidden for the deposition of k-mers. The defects of the k-mers are distributed randomly on the deposited k-mers. The sites filled with k-mers have high electrical conductivity, σk, whereas the empty sites, and the sites filled by either types of defect have a low electrical conductivity, σl, i.e., a high-contrast, σk /σl ≫ 1, is assumed. We examined isotropic (both the possible x and y orientations of a particle are equiprobable) and anisotropic (all particles are aligned along one given direction, y) deposition. To calculate the effective electrical conductivity, the monolayer was presented as a random resistor network and the Frank-Lobb algorithm was used. The effects of the concentrations of defects dl and dk on the electrical conductivity for the values of k =2n, where n = 1 , 2 , … , 5, were studied. Increase of both the dl and dk parameters values resulted in decreases in the value of σ and the suppression of percolation. Moreover, for anisotropic deposition the electrical conductivity along the y direction was noticeably larger than in the perpendicular direction, x. Phase diagrams in the (dl ,dk)-plane for different values of k were obtained.

Identifying the Effective Factors in Making Trust in Online Social Networks on the perspective of Iranian experts Using Fuzzy ELECTRE

Directory of Open Access Journals (Sweden)

Elham Haghighi

2015-12-01

Full Text Available this paper attempts to rank the effective factors in making trust in social networks to provide the possibility of attracting and increasing users’ trust on these social networks for providers and designers of online social networks. Identifying the effective factors in making trust in social networks is a multi-criteria decision making problem and most of effective factors are ambiguous and uncertain, thereby this article uses Fuzzy ELECTRE to rank them. By implementing Fuzzy ELECTRE on gathered data, respectively «usability factor», «supporting up to date technology factor», «integrity» and «the rate of ethics factor» are on the top of effective factors in making trust in users. In general, «web features» and «technology features» have a higher degree of importance than «security features», «individual-social features» and «cultural features». Ranking of Fuzzy ELECTRE comparison ranking of Fuzzy TOPSIS and Fuzzy ELECTRE method becomes validate because Spearman correlation coefficients is 0/867. Result of sensitivity analysis on changing weight of criteria shows that Fuzzy ELECTRE isn’t affected by ambiguity and uncertainty in inputs.

PAT Design Strategy for Energy Recovery in Water Distribution Networks by Electrical Regulation

Directory of Open Access Journals (Sweden)

Helena M. Ramos

2013-01-01

Full Text Available In the management of water distribution networks, large energy savings can be yielded by exploiting the head drop due to the network pressure control strategy, i.e., for leak reductions. Hydropower in small streams is already exploited, but technical solutions combining efficiency and economic convenience are still required. In water distribution networks, an additional design problem comes out from the necessity of ensuring a required head drop under variable operating conditions, i.e., head and discharge variations. Both a hydraulic regulation (HR—via a series-parallel hydraulic circuit- and an electrical regulation (ER—via inverter- are feasible solutions. A design procedure for the selection of a production device in a series-parallel hydraulic circuit has been recently proposed. The procedure, named VOS (Variable Operating Strategy, is based on the overall plant efficiency criteria and is applied to a water distribution network where a PAT (pump as a turbine is used in order to produce energy. In the present paper the VOS design procedure has been extended to the electrical regulation and a comparison between HR and ER efficiency and flexibility within a water distribution network is shown: HR was found more flexible than ER and more efficient. Finally a preliminary economic study has been carried out in order to show the viability of both systems, and a shorter payback period of the electromechanical equipment was found for HR mode.

Econometric model as a regulatory tool in electricity distribution. Case network performance assessment model

International Nuclear Information System (INIS)

Honkapuro, S.; Lassila, J.; Viljainen, S.; Tahvanainen, K.; Partanen, J.

2004-01-01

Electricity distribution companies operate in the state of natural monopolies since building of parallel networks is not cost- effective. Monopoly companies do not have pressure from the open markets to keep their prices and costs at reasonable level. The regulation of these companies is needed to prevent the misuse of the monopoly position. Regulation is usually focused either on the profit of company or on the price of electricity. Regulation method which determines allowed income for each company with generic computation model can be seen as an econometric model. In this document, the usability of an econometric model in the regulation of electricity distribution companies is evaluated. As the special case of an econometric model, the method called Network Performance Assessment Model, NPAM (Naetnyttomodellen in Swedish), is analysed. NPAM is developed by Swedish Energy Agency (STEM) for the regulation of electricity distribution companies. Both theoretical analysis and calculations of an example network area are presented in this document to find the major directing effects of the model. The parameters of NPAM, which are used in the calculations of this research report, were dated on 30th of March 2004. These parameters were most recent ones available at the time when analysis was done. However, since NPAM have been under development, the parameters have been constantly changing. Therefore slight changes might occur in the numerical results of calculations if they were made with the latest set of parameters. However, main conclusions are same and do not depend on exact parameters

Zero-dynamics principle for perfect quantum memory in linear networks

International Nuclear Information System (INIS)

Yamamoto, Naoki; James, Matthew R

2014-01-01

In this paper, we study a general linear networked system that contains a tunable memory subsystem; that is, it is decoupled from an optical field for state transportation during the storage process, while it couples to the field during the writing or reading process. The input is given by a single photon state or a coherent state in a pulsed light field. We then completely and explicitly characterize the condition required on the pulse shape achieving the perfect state transfer from the light field to the memory subsystem. The key idea to obtain this result is the use of zero-dynamics principle, which in our case means that, for perfect state transfer, the output field during the writing process must be a vacuum. A useful interpretation of the result in terms of the transfer function is also given. Moreover, a four-node network composed of atomic ensembles is studied as an example, demonstrating how the input field state is transferred to the memory subsystem and what the input pulse shape to be engineered for perfect memory looks like. (paper)

Zero-dynamics principle for perfect quantum memory in linear networks

Science.gov (United States)

Yamamoto, Naoki; James, Matthew R.

2014-07-01

In this paper, we study a general linear networked system that contains a tunable memory subsystem; that is, it is decoupled from an optical field for state transportation during the storage process, while it couples to the field during the writing or reading process. The input is given by a single photon state or a coherent state in a pulsed light field. We then completely and explicitly characterize the condition required on the pulse shape achieving the perfect state transfer from the light field to the memory subsystem. The key idea to obtain this result is the use of zero-dynamics principle, which in our case means that, for perfect state transfer, the output field during the writing process must be a vacuum. A useful interpretation of the result in terms of the transfer function is also given. Moreover, a four-node network composed of atomic ensembles is studied as an example, demonstrating how the input field state is transferred to the memory subsystem and what the input pulse shape to be engineered for perfect memory looks like.

Households' willingness to pay for overhead-to-underground conversion of electricity distribution networks

Energy Technology Data Exchange (ETDEWEB)

McNair, Ben J., E-mail: Ben.McNair@anu.edu.a [Crawford School of Economics and Government, Australian National University, Crawford Building, Lennox Crossing, Canberra ACT 0200 (Australia); Bennett, Jeff [Crawford School of Economics and Government, Australian National University, Crawford Building, Lennox Crossing, Canberra ACT 0200 (Australia); Hensher, David A.; Rose, John M. [Institute of Transport and Logistics Studies, The Business School, University of Sydney, NSW 2006 (Australia)

2011-05-15

Underground low-voltage electricity networks have several advantages over overhead networks including reliability of supply, safety and improved visual amenity. The economic viability of replacing existing overhead networks with new underground networks depends on the value of these benefits to households, but no complete value estimates are available in the literature. This paper represents a contribution towards addressing this research gap. A stated choice survey is used to estimate willingness to pay for undergrounding in established residential areas in Canberra, Australia. Average willingness to pay is at least A$6838 per household and there is significant variation in preferences over the population. The results suggest that benefits would be highest in areas with higher household income and older residents where visual amenity, safety, tree trimming, or restrictions on the use of yard space are of concern. - Research highlights: {yields} A choice experiment is used to estimate values for undergrounding electricity wires. {yields} Average willingness to pay is at least A$6838 per household in Canberra, Australia. {yields} Preferences vary considerably over the population. {yields} Benefits would be highest in areas with higher income and older residents.

Non-Linear Behaviour Of Gelatin Networks Reveals A Hierarchical Structure

KAUST Repository

Yang, Zhi; Hemar, Yacine; Hilliou, loic; Gilbert, Elliot P.; McGillivray, Duncan James; Williams, Martin A. K.; Chaieb, Saharoui

2015-01-01

We investigate the strain hardening behaviour of various gelatin networks - namely physically-crosslinked gelatin gel, chemically-crosslinked gelatin gels, and a hybrid gels made of a combination of the former two - under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillation shear protocols. Further, the internal structures of physically-crosslinked gelatin gel and chemically-crosslinked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically-crosslinked network, whereas in the physically-crosslinked gels a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as correlation length (ξ), cross-sectional polymer chain radius (Rc), and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physically-crosslinked and chemically crosslinked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized non-linear elastic theory we used to fit our stress-strain curves. The chemical crosslinking that generates coils and aggregates hinders the free stretching of the triple helices bundles in the physically-crosslinked gels.

Non-Linear Behaviour Of Gelatin Networks Reveals A Hierarchical Structure

KAUST Repository

Yang, Zhi

2015-12-14

We investigate the strain hardening behaviour of various gelatin networks - namely physically-crosslinked gelatin gel, chemically-crosslinked gelatin gels, and a hybrid gels made of a combination of the former two - under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillation shear protocols. Further, the internal structures of physically-crosslinked gelatin gel and chemically-crosslinked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically-crosslinked network, whereas in the physically-crosslinked gels a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as correlation length (ξ), cross-sectional polymer chain radius (Rc), and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physically-crosslinked and chemically crosslinked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized non-linear elastic theory we used to fit our stress-strain curves. The chemical crosslinking that generates coils and aggregates hinders the free stretching of the triple helices bundles in the physically-crosslinked gels.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

Science.gov (United States)

de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo

2018-03-01

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

Social representations of electricity network technologies: exploring processes of anchoring and objectification through the use of visual research methods.

Science.gov (United States)

Devine-Wright, Hannah; Devine-Wright, Patrick

2009-06-01

The aim of this study was to explore everyday thinking about the UK electricity network, in light of government policy to increase the generation of electricity from renewable energy sources. Existing literature on public perceptions of electricity network technologies was broadened by adopting a more socially embedded conception of the construction of knowledge using the theory of social representations (SRT) to explore symbolic associations with network technologies. Drawing and association tasks were administered within nine discussion groups held in two places: a Scottish town where significant upgrades to the local transmission network were planned and an English city with no such plans. Our results illustrate the ways in which network technologies, such as high voltage (HV) pylons, are objectified in talk and drawings. These invoked positive as well as negative symbolic and affective associations, both at the level of specific pylons, and the 'National Grid' as a whole and are anchored in understanding of other networks such as mobile telecommunications. We conclude that visual methods are especially useful for exploring beliefs about technologies that are widespread, proximal to our everyday experience but nevertheless unfamiliar topics of everyday conversation.

A multi-component nanocomposite screen-printed ink with non-linear touch sensitive electrical conductivity.

Science.gov (United States)

Webb, Alexander J; Szablewski, Marek; Bloor, David; Atkinson, Del; Graham, Adam; Laughlin, Paul; Lussey, David

2013-04-26

Printable electronics is an innovative area of technology with great commercial potential. Here, a screen-printed functional ink, comprising a combination of semiconducting acicular particles, electrically insulating nanoparticles and a base polymer ink, is described that exhibits pronounced pressure sensitive electrical properties for applications in sensing and touch sensitive surfaces. The combination of these components in the as-printed ink yield a complex structure and a large and reproducible touch pressure sensitive resistance range. In contrast to the case for some composite systems, the resistance changes occur down to applied pressures of 13 Pa. Current-voltage measurements at fixed pressures show monotonic non-linear behaviour, which becomes more Ohmic at higher pressures and in all cases shows some hysteresis. The physical basis for conduction, particularly in the low pressure regime, can be described in terms of field assisted quantum mechanical tunnelling.

A multi-component nanocomposite screen-printed ink with non-linear touch sensitive electrical conductivity

International Nuclear Information System (INIS)

Webb, Alexander J; Szablewski, Marek; Bloor, David; Atkinson, Del; Graham, Adam; Laughlin, Paul; Lussey, David

2013-01-01

Printable electronics is an innovative area of technology with great commercial potential. Here, a screen-printed functional ink, comprising a combination of semiconducting acicular particles, electrically insulating nanoparticles and a base polymer ink, is described that exhibits pronounced pressure sensitive electrical properties for applications in sensing and touch sensitive surfaces. The combination of these components in the as-printed ink yield a complex structure and a large and reproducible touch pressure sensitive resistance range. In contrast to the case for some composite systems, the resistance changes occur down to applied pressures of 13 Pa. Current–voltage measurements at fixed pressures show monotonic non-linear behaviour, which becomes more Ohmic at higher pressures and in all cases shows some hysteresis. The physical basis for conduction, particularly in the low pressure regime, can be described in terms of field assisted quantum mechanical tunnelling. (paper)

A multi-component nanocomposite screen-printed ink with non-linear touch sensitive electrical conductivity

Science.gov (United States)

Webb, Alexander J.; Szablewski, Marek; Bloor, David; Atkinson, Del; Graham, Adam; Laughlin, Paul; Lussey, David

2013-04-01

Printable electronics is an innovative area of technology with great commercial potential. Here, a screen-printed functional ink, comprising a combination of semiconducting acicular particles, electrically insulating nanoparticles and a base polymer ink, is described that exhibits pronounced pressure sensitive electrical properties for applications in sensing and touch sensitive surfaces. The combination of these components in the as-printed ink yield a complex structure and a large and reproducible touch pressure sensitive resistance range. In contrast to the case for some composite systems, the resistance changes occur down to applied pressures of 13 Pa. Current-voltage measurements at fixed pressures show monotonic non-linear behaviour, which becomes more Ohmic at higher pressures and in all cases shows some hysteresis. The physical basis for conduction, particularly in the low pressure regime, can be described in terms of field assisted quantum mechanical tunnelling.

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

Directory of Open Access Journals (Sweden)

Makii Muthalib

Full Text Available Neuroimaging studies have shown neuromuscular electrical stimulation (NMES-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC, premotor cortex (PMC, supplementary motor area (SMA, and secondary somatosensory area (S2, as well as regions of the prefrontal cortex (PFC known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI, and with reference to voluntary (VOL wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb and deoxygenated (HHb hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2. However, the level and area of contralateral sensorimotor network (including PFC activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

Science.gov (United States)

Muthalib, Makii; Re, Rebecca; Zucchelli, Lucia; Perrey, Stephane; Contini, Davide; Caffini, Matteo; Spinelli, Lorenzo; Kerr, Graham; Quaresima, Valentina; Ferrari, Marco; Torricelli, Alessandro

2015-01-01

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Optimal Control of Scalar Conservation Laws Using Linear/Quadratic Programming: Application to Transportation Networks

KAUST Repository

Li, Yanning

2014-03-01

This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.

Optimal Control of Scalar Conservation Laws Using Linear/Quadratic Programming: Application to Transportation Networks

KAUST Repository

Li, Yanning; Canepa, Edward S.; Claudel, Christian

2014-01-01

This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.

Essays on electricity markets

Energy Technology Data Exchange (ETDEWEB)

Rud, Linda

2009-07-01

The report covers several topics of electricity markets: The first essay, 'Selected Topics on Early Electricity Market Design in Norway' studies market design issues in establishing the market-based Norwegian electricity market. Essays 2-4 focus on issues of network congestion: 'Capacity Charges: A Price Adjustment Process for Managing Congestion in Electricity Transmission Networks' presents the capacity charge approach for managing transmission constraints in electricity networks. 'Understanding the Stochastics of Nodal Prices: Price Processes in a Constrained Network' seeks a further understanding of stochastic nodal prices processes. 'Investment Evaluation in a Constrained Electricity Network with Stochastic Nodal Price Processes' studies how the interaction of the competitive market and the capacitated network affects the evaluation of investments under uncertainty, and points out potential pitfalls of evaluation. In the last essay, 'A Newsboy Model Perspective on the Power Market: The Case of a Wind Power Producer' we discuss aspects of optimal bidding for a wind power producer. (Author)

Essays on electricity markets

Energy Technology Data Exchange (ETDEWEB)

Rud, Linda

2009-07-01

The report covers several topics of electricity markets: The first essay, 'Selected Topics on Early Electricity Market Design in Norway' studies market design issues in establishing the market-based Norwegian electricity market. Essays 2-4 focus on issues of network congestion: 'Capacity Charges: A Price Adjustment Process for Managing Congestion in Electricity Transmission Networks' presents the capacity charge approach for managing transmission constraints in electricity networks. 'Understanding the Stochastics of Nodal Prices: Price Processes in a Constrained Network' seeks a further understanding of stochastic nodal prices processes. 'Investment Evaluation in a Constrained Electricity Network with Stochastic Nodal Price Processes' studies how the interaction of the competitive market and the capacitated network affects the evaluation of investments under uncertainty, and points out potential pitfalls of evaluation. In the last essay, 'A Newsboy Model Perspective on the Power Market: The Case of a Wind Power Producer' we discuss aspects of optimal bidding for a wind power producer. (Author)

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

Monte Carlo simulations of electrical percolation in multicomponent thin films with nanofillers

Science.gov (United States)

Ni, Xiaojuan; Hui, Chao; Su, Ninghai; Jiang, Wei; Liu, Feng

2018-02-01

We developed a 2D disk-stick percolation model to investigate the electrical percolation behavior of an insulating thin film reinforced with 1D and 2D conductive nanofillers via Monte Carlo simulation. Numerical predictions of the percolation threshold in single component thin films showed good agreement with the previous published work, validating our model for investigating the characteristics of the percolation phenomena. Parametric studies of size effect, i.e., length of 1D nanofiller and diameter of 2D nanofiller, were carried out to predict the electrical percolation threshold for hybrid systems. The relationships between the nanofillers in two hybrid systems was established, which showed differences from previous linear assumption. The effective electrical conductance was evaluated through Kirchhoff’s current law by transforming it into a resistor network. The equivalent resistance was obtained from the distribution of nodal voltages by solving a system of linear equations with a Gaussian elimination method. We examined the effects of stick length, relative concentration, and contact patterns of 1D/2D inclusions on electrical performance. One novel aspect of our study is its ability to investigate the effective conductance of nanocomposites as a function of relative concentrations, which shows there is a synergistic effect when nanofillers with different dimensionalities combine properly. Our work provides an important theoretical basis for designing the conductive networks and predicting the percolation properties of multicomponent nanocomposites.

Assessment of the voltage level and losses with photovoltaic and electric vehicle in low voltage network

NARCIS (Netherlands)

Ye, G.; Xiang, Y.; Cobben, J.F.G.

2014-01-01

Livelab from Alliander, a network operator, is a program which started to measure electrical and power quality data in the Dutch distribution network since 2013. A proper probability distribution can be used to model load distribution on feeders. This paper presents a methodology to generate the

Computer network for electric power control systems. Chubu denryoku (kabu) denryoku keito seigyoyo computer network

Energy Technology Data Exchange (ETDEWEB)

Tsuneizumi, T. (Chubu Electric Power Co. Inc., Nagoya (Japan)); Shimomura, S.; Miyamura, N. (Fuji Electric Co. Ltd., Tokyo (Japan))

1992-06-03

A computer network for electric power control system was developed that is applied with the open systems interconnection (OSI), an international standard for communications protocol. In structuring the OSI network, a direct session layer was accessed from the operation functions when high-speed small-capacity information is transmitted. File transfer, access and control having a function of collectively transferring large-capacity data were applied when low-speed large-capacity information is transmitted. A verification test for the realtime computer network (RCN) mounting regulation was conducted according to a verification model using a mini-computer, and a result that can satisfy practical performance was obtained. For application interface, kernel, health check and two-route transmission functions were provided as a connection control function, so were transmission verification function and late arrival abolishing function. In system mounting pattern, dualized communication server (CS) structure was adopted. A hardware structure may include a system to have the CS function contained in a host computer and a separate installation system. 5 figs., 6 tabs.

Modelling and Simulation of the SVC for Power System Flow Studies: Electrical Network in voltage drop

Directory of Open Access Journals (Sweden)

Narimen Aouzellag LAHAÇANI

2008-12-01

Full Text Available The goal of any Flexible AC Transmission Systems (FACTS devices study is to measure their impact on the state of the electrical networks into which they are introduced. Their principal function is to improve the static and dynamic properties of the electrical networks and that by increasing the margins of static and dynamic stability and to allow the power transit to the thermal limits of the lines.To study this impact, it is necessary to establish the state of the network (bus voltages and angles, powers injected and forwarded in the lines before and after the introduction of FACTS devices. This brings to calculate the powers transit by using an iterative method such as Newton-Raphson. Undertaking a calculation without the introduction of FACTS devices followed by a calculation with the modifications induced by the integration of FACTS devices into the network, makes it possible to compare the results obtained in both cases and thus assess the interest of the use of devices FACTS.

Aperiodic linear networked control considering variable channel delays: application to robots coordination.

Science.gov (United States)

Santos, Carlos; Espinosa, Felipe; Santiso, Enrique; Mazo, Manuel

2015-05-27

One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

Aperiodic Linear Networked Control Considering Variable Channel Delays: Application to Robots Coordination

Directory of Open Access Journals (Sweden)

Carlos Santos

2015-05-01

Full Text Available One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

Improvements in the computation of balances and localization of commercial losses of electric energy in 0.38-kV networks

International Nuclear Information System (INIS)

Vorotnitskii, V. Z.; Zaslonov, S. V.; Lysyuk, S. S.

2006-01-01

An example of four 0.38-kV overhead distribution lines fed from a 10/0.4-kV transformer substation of the Grodno Electrical Networks (Belarus') and supplying electricity to a cottage area is used for analyzing energy balances and commercial losses in the networks. It is shown that the reliability of such analysis can be ensured by the use of modern software and electronic energy meters recording a retrospective of half-hourly intervals of active and reactive electric energy

A modular neural network scheme applied to fault diagnosis in electric power systems.

Science.gov (United States)

Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

2014-01-01

This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations.

Science.gov (United States)

Xiao, Lin; Liao, Bolin; Li, Shuai; Chen, Ke

2018-02-01

In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of the network capacities and possible congestion of the electricity transmission networks within the accession countries

International Nuclear Information System (INIS)

Kristiansen, Tarjei; Hewicker, Cristian

2005-01-01

cross-border network constraints. Besides the technical parameters to describe production and main network characteristics, the main remaining input factors are costs/fuel prices and demand.The outputs of the PROSYM tool are generation quantities, costs, short-run marginal prices and flows. To calibrate the model and to check its accuracy, we have first performed simulations for the year 2003 (the reference year) where information on actual market prices and (physical) cross-border flows is available. Thereafter, we have run additional simulations for the years 2010 and 2020 to analyse the future development of generation, export and import patterns, and to assess the influence of new interconnector projects. Due to uncertainty about future development, we have additionally tested the sensitivity of our base case to changes in e.g. the rate of growth in generation capacity and demand, the future generation structure, or the development of fuel prices. With a view to the economic import and export potential, our investigations confirm the assumption that network constraints represent a major barrier to the free exchange of electricity within the IEM. Considering the frequency and severity of congestion, our calculations show major network constraints at the following borders: 1) From Central Eastern Europe (Poland, Czech Republic, Slovakia) to Germany, Austria and Hungary; 2) From Austria and Slovenia to Italy; 3) From Bulgaria and Serbia and Montenegro (respectively Macedonia) to Greece. Our results show, not unexpectedly, considerable differences in the net benefits of individual transmission capacity increases, due to differences in market value and/or investment costs. (Author)

Node localization algorithm of wireless sensor networks for large electrical equipment monitoring application

DEFF Research Database (Denmark)

Chen, Qinyin; Hu, Y.; Chen, Zhe

2016-01-01

Node localization technology is an important technology for the Wireless Sensor Networks (WSNs) applications. An improved 3D node localization algorithm is proposed in this paper, which is based on a Multi-dimensional Scaling (MDS) node localization algorithm for large electrical equipment monito...

A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

International Nuclear Information System (INIS)

Azadeh, A.; Ghaderi, S.F.; Sohrabkhani, S.

2008-01-01

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to

Network access charges, vertical integration, and property rights structure - experiences from the German electricity markets

International Nuclear Information System (INIS)

Growitsch, C.; Wein, T.

2005-01-01

After the deregulation of the German electricity markets in 1998, the German government opted for a regulatory regime called negotiated third party access, which would be subject to ex-post control by the federal cartel office. Network access charges for new competitors are based on contractual arrangements between energy producers and industrial consumers. As the electricity networks are incontestable natural monopolies, the local and regional network operators are able to set (monopolistic) charges at their own discretion, restricted only by the possible interference of the federal cartel office (Bundeskartellamt). In this paper we analyze if there is evidence for varying charging behaviour depending on the supplier's economic independence (structure of property rights) or its level of vertical integration. For this purpose, we hypothesise that incorporated and vertically integrated suppliers set different charges than independent utility companies. Multivariate estimations show a relation between network access charges and the network operator's economic independence as well as level of vertical integration: on the low voltage level for an estimated annual consumption of 1700 kW/h, vertically integrated firms set-in accordance with our hypothesis-significantly lower access charges than vertically separated suppliers, whereas incorporated network operators charge significantly higher charges compared to independent suppliers. These results could not have been confirmed for other consumptions or voltage levels. (author)

Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

Science.gov (United States)

Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

2016-03-01

The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling Geoelectric Fields and Geomagnetically Induced Currents Around New Zealand to Explore GIC in the South Island's Electrical Transmission Network

Science.gov (United States)

Divett, T.; Ingham, M.; Beggan, C. D.; Richardson, G. S.; Rodger, C. J.; Thomson, A. W. P.; Dalzell, M.

2017-10-01

Transformers in New Zealand's South Island electrical transmission network have been impacted by geomagnetically induced currents (GIC) during geomagnetic storms. We explore the impact of GIC on this network by developing a thin-sheet conductance (TSC) model for the region, a geoelectric field model, and a GIC network model. (The TSC is composed of a thin-sheet conductance map with underlying layered resistivity structure.) Using modeling approaches that have been successfully used in the United Kingdom and Ireland, we applied a thin-sheet model to calculate the electric field as a function of magnetic field and ground conductance. We developed a TSC model based on magnetotelluric surveys, geology, and bathymetry, modified to account for offshore sediments. Using this representation, the thin sheet model gave good agreement with measured impedance vectors. Driven by a spatially uniform magnetic field variation, the thin-sheet model results in electric fields dominated by the ocean-land boundary with effects due to the deep ocean and steep terrain. There is a strong tendency for the electric field to align northwest-southeast, irrespective of the direction of the magnetic field. Applying this electric field to a GIC network model, we show that modeled GIC are dominated by northwest-southeast transmission lines rather than east-west lines usually assumed to dominate.

Theory of linear physical systems theory of physical systems from the viewpoint of classical dynamics, including Fourier methods

CERN Document Server

Guillemin, Ernst A

2013-01-01

An eminent electrical engineer and authority on linear system theory presents this advanced treatise, which approaches the subject from the viewpoint of classical dynamics and covers Fourier methods. This volume will assist upper-level undergraduates and graduate students in moving from introductory courses toward an understanding of advanced network synthesis. 1963 edition.

Decision support for the definition of wind turbine systems adequacy to site specificities and weak electrical networks

International Nuclear Information System (INIS)

Arbaoui, A.

2006-10-01

A decision support system for the definition of wind turbine systems is developed by taking into account the wind and site characteristics, the wind turbine components and the electrical network properties close to the site. The approach is based on functional analysis, on the investigation of the functional fluxes and on the definition of a model suitable for supporting decision at the preliminary stages of wind turbine design. The complete set of solutions derived from the model is determined using a Constraint Satisfaction Problem solver. The intrinsic capability of the model to support decision is derived from the investigation of the model parsimony, precision, exactness and specialization. The model takes into account performance criteria resulting from knowledge of manufacturers, distributors and investors. These criteria are used to discriminate design alternatives. Design alternatives correspond to choices of site (wind, electric network) and wind turbine architectures (related to 7 design variables). Performance criteria are the cost of electric kWh, the amount of energy being produced and the discounted total cost of the project. Electric network connection to wind turbines is taken into account through slow variations of the voltage and Flickers phenomenon. First, the maximal rate of penetration of the wind turbine energy production is determined. Next, two design alternatives have been investigated to improve wind turbine system integration in electric distribution networks. These alternatives are a reactive power control system and an inertial energy storage system. Inertial storage systems seem to be more expensive than reactive power control systems for this type of application. The influence of site specificities on decision making process has been established through three different sites (a Mediterranean site and two sites located in northern Europe). Profits relative to the cost of kWh appear to be high for Mediterranean sites. Most of the

Policy and network regulation for the integration of distribution generation and renewables for electricity supply

International Nuclear Information System (INIS)

Ten Donkelaar, M.; Van Oostvoorn, F.

2005-08-01

This study has analysed the existing policy and regulation aimed at the integration of an increased share of Distributed Generation (DG) in electricity supply systems in the European Union. It illustrates the state of the art and progress in the development of support mechanisms and network regulation for large-scale integration of DG. Through a benchmark study a systematic comparison has been made of different DG support schemes and distribution network regulation in EU Member States to a predefined standard, the level playing field. This level playing field has been defined as the situation where energy markets, policy and regulation provide neutral incentives to central versus distributed generation, which results in an economically more efficient electricity supply to the consumer. In current regulation and policy a certain discrepancy can be noticed between the actual regulation and policy support systems in a number of countries, the medium to long term targets and the ideal situation described according to the level playing field objective. Policies towards DG and RES are now mainly aimed at removing short-term barriers, increasing the production share of DG/RES, but often ignoring the more complex barriers of integrating DG/RES that is created by the economic network regulation in current electricity markets

Networked control of discrete-time linear systems over lossy digital communication channels

Science.gov (United States)

Jin, Fang; Zhao, Guang-Rong; Liu, Qing-Quan

2013-12-01

This article addresses networked control problems for linear time-invariant systems. The insertion of the digital communication network inevitably leads to packet dropout, time delay and quantisation error. Due to data rate limitations, quantisation error is not neglected. In particular, the case where the sensors and controllers are geographically separated and connected via noisy, bandwidth-limited digital communication channels is considered. A fundamental limitation on the data rate of the channel for mean-square stabilisation of the closed-loop system is established. Sufficient conditions for mean-square stabilisation are derived. It is shown that there exists a quantisation, coding and control scheme to stabilise the unstable system over packet dropout communication channels if the data rate is larger than the lower bound proposed in our result. An illustrative example is given to demonstrate the effectiveness of the proposed conditions.

Feedback linearizing control of a MIMO power system

Science.gov (United States)

Ilyes, Laszlo

Prior research has demonstrated that either the mechanical or electrical subsystem of a synchronous electric generator may be controlled using single-input single-output (SISO) nonlinear feedback linearization. This research suggests a new approach which applies nonlinear feedback linearization to a multi-input multi-output (MIMO) model of the synchronous electric generator connected to an infinite bus load model. In this way, the electrical and mechanical subsystems may be linearized and simultaneously decoupled through the introduction of a pair of auxiliary inputs. This allows well known, linear, SISO control methods to be effectively applied to the resulting systems. The derivation of the feedback linearizing control law is presented in detail, including a discussion on the use of symbolic math processing as a development tool. The linearizing and decoupling properties of the control law are validated through simulation. And finally, the robustness of the control law is demonstrated.

Passive quantum error correction of linear optics networks through error averaging

Science.gov (United States)

Marshman, Ryan J.; Lund, Austin P.; Rohde, Peter P.; Ralph, Timothy C.

2018-02-01

We propose and investigate a method of error detection and noise correction for bosonic linear networks using a method of unitary averaging. The proposed error averaging does not rely on ancillary photons or control and feedforward correction circuits, remaining entirely passive in its operation. We construct a general mathematical framework for this technique and then give a series of proof of principle examples including numerical analysis. Two methods for the construction of averaging are then compared to determine the most effective manner of implementation and probe the related error thresholds. Finally we discuss some of the potential uses of this scheme.

Transition metal modified bulk BiFeO3 with improved magnetization and linear magneto-electric coupling

International Nuclear Information System (INIS)

Puli, Venkata Sreenivas; Kumar, A.; Panwar, N.; Panwar, I.C.; Katiyar, R.S.

2011-01-01

Highlights: → Present composition (Bi 0.9 Sm 0.10 Fe 0.95 Co 0.05 O 3 (BSFCO) have shown very high magnetization compared to parent BFO. → The magnetic hysteresis loops are well saturated with high saturation magnetization 2.89 emu/gm (unpoled and unleached) and 2.18 emu/gm (poled and unleached) respectively. → Converse ME coupling were found 0.8e-10 s m -1 (H||E) and 0.6-0.8 x 10 -10 s m -1 (H-perpendicular E) which are better than the single phase multiferroic obeying linear ME coupling. - Abstract: At present BiFeO 3 (BFO) is the most attractive and sole example, which possesses low magnetization value, high leakage current and low polarization in ceramic form. Single-phase room temperature multiferroics are rare in nature. This paper deals with the improved magnetic and observed linear magneto-electric coupling in Co and Sm co-doped BiFeO 3 ceramics synthesized by sol-gel process at low temperature ∼600 deg. C. As synthesized Bi 0.9 Sm 0.10 Fe 0.95 Co 0.05 O 3 (BSFCO) showed high impurities phases (20%) over wide range of calcination temperatures. Impurity phases reduced drastically from 20% to 5% after leaching with nitric acid. However the electrical and the magnetic properties were almost the same for both phases. Well-defined magnetic hysteresis with high magnetic moment was found at room temperature. Ferroelectric polarization studies demonstrated similar values and shape as reported in literature for the pure bulk BFO. Linear magneto-electric (ME) coupling and weak ME coefficient (α) ∼ 0.6 e-10 s m -1 were observed in the co-doped BFO. The origin of the strong ferromagnetic property in our samples may be due to the presence of rare earth and transition metal ions at the lattice sites of BFO or due to impurity phase, since we have not seen any change in magnetization with reduction of impurity phase the later effect is more unlikely.

Undelivered electricity as an indicator of the effects of automation in the 10 kV network PD ED Belgrade

Directory of Open Access Journals (Sweden)

Vrcelj Nada

2013-01-01

Full Text Available The paper discusses the effects of automation in the 10 kV PD ED Belgrade valorized through undelivered electricity. In the paper it was observed the parts of the network for which it was possible to reconstruct the events of the past. Calculations undelivered electricity were carried out for the period prior to the implementation of recloser in remote control system and during the period of probation system. A significant reduction in the duration of the fault, and thus undelivered electricity in automated network, indicates an increase in the reliability level after the implementation in the system SCADA SN.

A SURVEY OF SMART ELECTRICAL BOARDS IN UBIQUITOUS SENSOR NETWORKS FOR GEOMATICS APPLICATIONS

Directory of Open Access Journals (Sweden)

S. M. R. Moosavi

2015-12-01

Full Text Available Nowadays more advanced sensor networks in various fields are developed. There are lots of online sensors spreading around the world. Sensor networks have been used in Geospatial Information Systems (GIS since sensor networks have expanded. Health monitoring, environmental monitoring, traffic monitoring, etc, are the examples of its applications in Geomatics. Sensor network is an infrastructure comprised of sensing (measuring, computing, and communication elements that gives an administrator the ability to instrument, observe, and react to events and phenomena in a specified environment. This paper describes about development boards which can be used in sensor networks and their applications in Geomatics and their role in wireless sensor networks and also a comparison between various types of boards. Boards that are discussed in this paper are Arduino, Raspberry Pi, Beagle board, Cubieboard. The Boards because of their great potential are also known as single board computers. This paper is organized in four phases: First, Reviewing on ubiquitous computing and sensor networks. Second, introducing of some electrical boards. Then, defining some criterions for comparison. Finally, comparing the Ubiquitous boards.

ELECTRIC MOTOR DIAGNOSTICS OF SWITCHES BASED ON THE NEURAL NETWORK DATA MODELING THE SPECTRAL DECOMPOSITION OF THE CURRENTS

Directory of Open Access Journals (Sweden)

O. M. Shvets

2009-07-01

Full Text Available The method of automated diagnostics of electric motors is offered. It uses a neural network revealing the electric motor faults on the basis of analysis of frequency spectrum of current flowing through the motor.

Electrical equivalent thermal network for direct contact membrane distillation modeling and analysis

KAUST Repository

Karam, Ayman M.

2016-09-19

Membrane distillation (MD) is an emerging water desalination technology that offers several advantages compared to conventional desalination methods. Although progress has been made to model the physics of the process, there are two common limitations of existing models. Firstly, many of the models are based on the steady-state analysis of the process and secondly, some of the models are based on partial differential equations, which when discretized introduce many states which are not accessible in practice. This paper presents the derivation of a novel dynamic model, based on the analogy between electrical and thermal systems, for direct contact membrane distillation (DCMD). An analogous electrical thermal network is constructed and its elements are parameterized such that the response of the network models the DCMD process. The proposed model captures the spatial and temporal responses of the temperature distribution along the flow direction and is able to accurately predict the distilled water flux output. To demonstrate the adequacy of the proposed model, validation with time varying and steady-state experimental data is presented. (C) 2016 Elsevier Ltd. All rights reserved.

Exploring the determinants of “best practice” benchmarking in electricity network regulation

International Nuclear Information System (INIS)

Brophy Haney, Aoife; Pollitt, Michael G.

2011-01-01

In this paper we use a best practice index constructed from the survey responses of regulators in 40 countries to explore the determinants of the methods used in electricity network regulation. Drawing from the incentive regulation and institutional economics literature, we construct an empirical model to investigate the impact of industry size, political and economic institutions on the degree of best practice regulation. Our results suggest that the existence and experience of an independent regulator is the most important institutional determinant of best practice regulation. In addition, small numbers of network companies act as a constraint on the use of advanced benchmarking methods. Finally, regional effects are also important. These findings lead us to question whether one “best practice” model is in fact applicable to countries with very different political and economic contexts. - Highlights: ► Political institutions are strong determinants of best practice electricity regulation. ► Behavior of neighboring regulators may shape regulatory outcomes. ► One “best practice” model may not be appropriate.

Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks

Directory of Open Access Journals (Sweden)

Reza Ahmadi Kordkheili

2016-01-01

Full Text Available A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs in distribution networks by intelligent management of plug-in electric vehicle (PEV storage. The proposed algorithm is defined to manage the reverse power flow (PF from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner’s quality of service (QoS. The method uses genetic algorithm (GA to increase photovoltaic (PV penetration without jeopardizing PEV owners’ (QoS and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage.

SERS investigations and electrical recording of neuronal networks with three-dimensional plasmonic nanoantennas (Conference Presentation)

Science.gov (United States)

De Angelis, Francesco

2017-06-01

SERS investigations and electrical recording of neuronal networks with three-dimensional plasmonic nanoantennas Michele Dipalo, Valeria Caprettini, Anbrea Barbaglia, Laura Lovato, Francesco De Angelis e-mail: francesco.deangelis@iit.it Istituto Italiano di Tecnologia, Via Morego 30, 16163, Genova Biological systems are analysed mainly by optical, chemical or electrical methods. Normally each of these techniques provides only partial information about the environment, while combined investigations could reveal new phenomena occurring in complex systems such as in-vitro neuronal networks. Aiming at the merging of optical and electrical investigations of biological samples, we introduced three-dimensional plasmonic nanoantennas on CMOS-based electrical sensors [1]. The overall device is then capable of enhanced Raman Analysis of cultured cells combined with electrical recording of neuronal activity. The Raman measurements show a much higher sensitivity when performed on the tip of the nanoantenna in respect to the flat substrate [2]; this effect is a combination of the high plasmonic field enhancement and of the tight adhesion of cells on the nanoantenna tip. Furthermore, when plasmonic opto-poration is exploited [3] the 3D nanoelectrodes are able to penetrate through the cell membrane thus accessing the intracellular environment. Our latest results (unpublished) show that the technique is completely non-invasive and solves many problems related to state-of-the-art intracellular recording approaches on large neuronal networks. This research received funding from ERC-IDEAS Program: "Neuro-Plasmonics" [Grant n. 616213]. References: [1] M. Dipalo, G. C. Messina, H. Amin, R. La Rocca, V. Shalabaeva, A. Simi, A. Maccione, P. Zilio, L. Berdondini, F. De Angelis, Nanoscale 2015, 7, 3703. [2] R. La Rocca, G. C. Messina, M. Dipalo, V. Shalabaeva, F. De Angelis, Small 2015, 11, 4632. [3] G. C. Messina et al., Spatially, Temporally, and Quantitatively Controlled Delivery of

Synthesis of CdS nanowire networks and their optical and electrical properties

International Nuclear Information System (INIS)

Ma, R M; Wei, X L; Dai, L; Huo, H B; Qin, G G

2007-01-01

High quality single-crystal CdS nanowire (NW) networks have been synthesized on Si(111) substrates via the chemical vapour deposition method. X-ray diffraction and selected area electron diffraction show that the NWs in the networks grow along the directions and their (0001) crystal planes are parallel to the Si(111) substrates. Room-temperature photoluminescence (PL) spectra of single CdS NWs in the networks are dominated by a near-band-edge emission and free from deep-level defect emissions. The PLs resulting from free-exciton and bound-exciton recombinations are detected at 77 K. The results of the electrical transport measurement on the CdS NW networks show that the current can flow through different NWs via the cross-junctions. The resistivity, electron concentration and electron mobility of single NWs in the networks are estimated by fitting the I-V curves measured on single NWs with the metal-semiconductor-metal model suggested by Zhang et al (2006 Appl. Phys. Lett. 88 073102; 2007 Adv. Funct. Mater. at press)