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Particle swarm optimization for power system state estimation

NeuroCOMPUTING 2015年 148卷 175-180页

作者： Tungadio, D. H. Numbi, B. P. Siti, M. W. Jimoh, A. A. Tshwane Univ Technol Dept Elect Engn ZA-0001 Pretoria South Africa

The electrical network measurements are usually sent to the control centers using specific communication protocols. However, these measurements contain uncertainties due to the meters and communication errors (noise), incomplete metering or unavailability of some of these measurements. The aim of state estimation is to estimate the state variables of the power system by minimizing all measurement errors available at the control center. In the past, many traditional algorithms, based on gradient approach, have been used for this purpose. This paper discusses the application of an artificial intelligence (AI) algorithm, the particle swarm optimization (PSO), to solve the state estimation problem within a power system. Two objective functions are formulated: the weighted least square (WLS) and weighted least absolute value (WLAV). The effectiveness of PSO over another AI optimization algorithm, genetic algorithm (GA), is shown by comparing both two solutions to the true state variable values obtained using Newton-Raphson (NR) algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： State estimation Power system Artificial intelligence Particle swarm optimization Genetic algorithm

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Performance Enhancement for Audio-Video Proxy Server 3

Performance Enhancement for Audio-Video Proxy Server

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3rd International conference on Frontiers in Intelligent Computing - Theory and Applications (FICTA)

作者： Kanrar, Soumen Mandal, Niranjan Kumar Vidyasagar Univ CS Dept Midnapore 02 WB India

ISBN: (纸本)9783319119328

Bandwidth optimization for real time traffic transmission is a challenging issue for the next generation network. The self control of the traffic rate to the web cache is based on the relative data present in the proxy server or to import the data from the data center node. The smoothness of traffic transmission highly depends on the self configuration of the cache memory of the web proxy server by the optimized page replacement procedure. The web proxy cache memory is finite in size so the enhancement of the system performance depends upon the self optimization of the traffic rate. This paper address the effect of the zipf like distribution parameters to the input traffic request and the effect of the parameters to measure of the bandwidth requirement for the desire Audio/Video data file. This paper presents the result for bandwidth optimization and traffic control depends on the size of the Audio/video file and the active duration of the transmission stream.

关键词： Cache Memory Zipf Distribution Page Replacement Traffic control Bandwidth optimization Self Configuration

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Simulation and optimization models of steady-state gas transmission networks 3rd

Simulation and optimization models of steady-state gas trans...

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3rd Trondheim Gas Technology conference

作者： Bermudez, Alfredo Gonzalez-Diaz, Julio Gonzalez-Dieguez, Francisco J. Gonzalez-Rueda, Angel M. Fernandez de Cordoba, Maria P. Univ Santiago de Compostela Dept Appl Math Santiago De Compostela 15782 Spain Univ Santiago de Compostela Dept Stat & Operat Res Santiago De Compostela 15782 Spain

Managing a gas transport network is a complex problem because of the number of possibilities of routing the gas through the pipes. The most important aim in this kind of systems is to fulfill the demand within the pressure bounds, independently of its associated costs. However, in the present work some cost drivers are also taken into account by means of different objective functions in order to manage the network in an efficient way. This work deals with mathematical modeling and optimization of gas transport networks, where a two-stage procedure is proposed. In the first stage, optimization algorithms based on mathematical programming are applied to make some decisions (whether to activate compressor stations, control valves and other control elements) and gives an initial solution to the second stage. This last stage, which is based on control theory techniques, refines the solution to obtain more accurate results. Due to the reduced complexity in each stage, both can be solved within reasonable runtimes for relatively large gas networks. Based on the mathematical methods involved, a software called GANESO (TM) has been developed. (C) 2015 The Authors. Published by Elsevier Ltd.

关键词： gas network optimization mixed integer programming nonlinear problem sequential linear programming control theory

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A Method of control by Telecommunication Airsystems for Wireless AD HOC networks optimization 3

A Method of Control by Telecommunication Airsystems for Wire...

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2015 IEEE 3rd International conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD)

作者： Lysenko, O. I. Valuiskyi, S. V. Tachinina, O. M. Danylyuk, S. L. Natl Tech Univ Ukraine KPI Kiev Ukraine Natl Aviat Univ Kiev Ukraine Cent Res Inst Armed Forces Ukraine Kiev Ukraine

ISBN: (纸本)9781467365673

In this paper we define the structural information connectivity of wireless ad-hoc networks, which takes into account not only the structural connections, but also ensure information exchange between a given pair of sender-recipient. To quantify the degree of structural information connectivity suggested two indicators: the capacity of the network and k-connectivity. Proposed a method for controlling the topology of the network overhead of aerial repeaters to improve each of the proposed indicators.

关键词： wireless ad hoc network unmanned aerial vehicle connectivity topology control

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Research on Small Hydropower Generation Forecasting Method Based on Improved BP Neural network

Research on Small Hydropower Generation Forecasting Method B...

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2016 3rd International conference on Materials Engineering,Manufacturing Technology and control(ICMEMTC 2016)

作者： LI Miao DENG Chang Hong TAN Jin YANG Wei ZHENG Li

Small hydro-power generation shows strong uncertainty, which greatly affects the load forecasting work in small hydropower regions. Thus it's important to improve the accuracy of small hydropower generation load forecasting. At present, the most commonly used forecasting method is artificial neural network, which has strong adaptability and learning ability but poor generalization and easily falls into local minimum. The random fluctuation of small hydropower is not taken into consideration. This paper was based on analyzing the characteristics of small hydropower generation load, combining the wavelet transform to decompose the historical load to establish the prediction model for each component feature. Particle Swarm optimization(Algorithm) was used to optimize initial weights and thresholds of neural networks before the prediction. After verified by real case in a rich small hydropower area in some province, the load prediction precision reaches 93.7%, higher than the precision of the high-voltage system criteria for assessing. The accuracy and effectiveness of the method is verified.

关键词： Correlation Analysis Small hydropower forecast BP network Wavelet Decomposition

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Multi area load frequency control using particle swarm optimization and fuzzy rules 3

Multi area load frequency control using particle swarm optim...

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3rd International conference on Recent Trends in Computing (ICRTC)

作者： Dhillon, Sukhwinder Singh Lather, Jagdeep Singh Marwaha, Sanjay SLIET Elect & Instrumentat Engg Sangrur 148016 Punjab India NIT Dept Elect Engn Kurukshetra 136119 Haryana India

This paper present heuristics based study of multi area power network. Heuristic procedures involving Particle Swarm Intelligence and Fuzzy based inferences have been employed to effectively obtain the optimized gains of PID controller. Any change in the load demand causes generator's shaft speed lower than the pre-set value and the system frequency deviates from the standard value results in malfunctioning of frequency relays. A five area load frequency model is constructed in Matlab/simulink by implementing the PID (Proportional, Integral and Differential) controllers to control the frequency deviations. The effect of interconnection of multi area power system as ring connection has been discussed. Simulations performed show the effectiveness of the current approach over simple fuzzy inferences in terms of performance as well as execution efficiency. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Load Frequency control Particle Swarm optimization Fuzzy Rules Error Analysis

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The Research on the control system of the autonomous vehicle for sea floor prospecting with chaotic optimization theory

The Research on the control system of the autonomous vehicle...

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2015 3rd International conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Zou En Huo Qing Huang Shuihong Chen Shengchuang College of zhujiang.South China Agricultural University College of Engineer.South China Agricultural University

ISBN: (纸本)9781510814912

In order to ensure the accuracy and precision about the trace of the autonomous vehicle for the sea floor prospecting, double- ring, series –connection closed-loop control mode is used in the paper. Fuzzy controller is used for controlling velocity in the inner-ring, and fuzzy neutral network controller is used in the out ring. For optimizing the control performance, chaotic algorithm is used to optimize the parameters in the fuzzy controller and the structure, weights and membership function in the fuzzy neural network controller. The experimental results show: Quick response speed, better self-adaptive performance and higher control quality are obtained in the system.

关键词： Fuzzy The Research on the control system of the autonomous vehicle for sea floor prospecting with chaotic optimization theory

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Study of Dynamic Responses of an Interconnected Two-Area all Thermal Power System with Governor and Boiler Nonlinearities using BBO 3

Study of Dynamic Responses of an Interconnected Two-Area all...

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3rd International conference on Computer, Communication, control and Information Technology (C3IT)

作者： Guha, Dipayan Roy, Provas Kumar Banerjee, Subrata NIT Durgapur EE Dept Durgapur W Bengal India Dr BC Roy Engn Coll EE Dept Durgapur W Bengal India

ISBN: (纸本)9781479944453

This paper aims to investigate the application of biogeography based optimization (BBO) technique to load frequency control (LFC) problem for improving power system dynamics with governor dead band and boiler dynamics type of nonlinearities. Two-area all thermal types interconnected power system network is considered for design and analysis purpose. The thermal areas are equipped with single and double reheat turbine, simultaneously. The designed problem is considered as an optimization problem and BBO algorithm is applied to search optimal gains of classical PID-controller which is used in AGC problem. The behavior of the test system is also investigated with the analysis towards the different cost functions such as integral square error (ISE) and integral time absolute error (ITAE). Small load perturbation (SLP) is considered in area-1 for studying the dynamic behaviors of the designed power system unit. To validate the effectiveness and superiority of proposed controller, the performances are compared with the results obtained by differential evaluation (DE) and particle swarm optimization (PSO) techniques. Simulation study exhibits significant effect of the designed controller on the dynamics of concerned power system network.

关键词： Automatic generation control Area control error Dead-band Boiler dynamics Integral square error Integral time absolute error Biogeography based optimization

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Improvement of the training and normalization method of artificial neural network in the prediction of indoor environment 9

Improvement of the training and normalization method of arti...

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9th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC) / 3rd International conference on Building Energy and Environment (COBEE)

作者： Zhang, Tianhu You, Xueyi Tianjin Univ Tianjin Key Lab Indoor Air Environm Qual Control Sch Environm Sci & Engn Tianjin 300072 Peoples R China

When designing the indoor environment based on computational fluid dynamics (CFD), the artificial neural network (ANN) playing a role of surrogate model of CFD is involved to reduce the computational cost. To improve the performance of ANN, the training and normalization method of ANN are studied. An MD-82 aircraft cabin is used to test the proposed method, and different environmental parameters are used to evaluate the cabin environment. The results of different training methods are compared, and the parallel combination of genetic algorithm and particle swarm optimization shows better prediction accuracy than other training methods. A local logarithm normalization method is proposed to improve the success rate of ANN prediction. The success rate is increased by 2.5 similar to 11.0% when the proposed local logarithm normalization method is adopted instead of local linear normalization one. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Artificial neural network Normalization Indoor environment CFD

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S-System Based Gene Regulatory network Reconstruction Using Firefly Algorithm 3

S-System Based Gene Regulatory Network Reconstruction Using ...

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3rd International conference on Computer, Communication, control and Information Technology (C3IT)

作者： Mandal, Sudip Saha, Goutam Pal, Rajat K. GIMT ECE Dept Krishna Nagar India NEHU IT Dept Shillong Meghalaya India Univ Calcutta CSE Dept Kolkata India

ISBN: (纸本)9781479944453

The correct inference of gene regulatory network plays a critical role in understanding biological regulation in cells and genome based therapeutics. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Since S-system is based on the rate law, it is considered as a suitable mathematical model for representing complex biological reactions between genes. As, this problem has multiple solutions, optimized solution need to be identified via different nature inspired metaheuristic algorithms. So, in this paper, a new method is elaborated that helps to infer gene regulatory network for Lung Adenocarcinoma using S-system and Firefly optimization which is an efficient but simple metaheuristic inspired by natural motion of fireflies. By optimizing the values of parameters of the S-system model, gene network can be easily reconstructed and inferred. Though direct biological validation of the network is not possible, but accuracy of the proposed method can be described as how well the network fit with the initial training data for different genes which is quite satisfactory for our research work.

关键词： Gene Regulatory network Microarray data S-stem Firefly Algorithm

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