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基于神经网络的电力系统状态估计

减小字体

增大字体作者：佚名来源：本站整理发布时间：2010-08-28 00:56:48

基于神经网络的电力系统状态估计①

韩富春　王娟娟

（太原理工大学电气与动力工程学院　太原　030024）

摘要　本文以Tank和Hopfield神经网络为基础，建立了一种由主从网络构成的电力系统状态估计神经网络模型。理论分析和实例模拟结果表明：该网络是稳定的，该方法是可行有效的。
关键词　状态估计　电力系统　神经网络

1　INTRODUCTION
　　Among the current state estimators，due togood estimation qualities and astringency，weightedleast estimator is a classical algorithm and an aca－demic basis．Butit also has some shortcomingssuchas the calculation of matrices．The paper applies aneural network modelto solve the real－time leastsquares（RLS）problem．Theoretical analysis andsimulations prove that this network is very suitableto solve this kind of problem and has greatly im－proved on the traditionalpower state estimation al－gorithm．
2　A MODEL OF WEIGHTED LEAST SQUARESALOGORITHM
    The observation equation ofpower systemstate estimation is nonlinear and can be linear as:
    z＝Hx＋v   （1）
where x isan dimension state vector；z isa mdimen－sion measurement vector；v is a measurement errorvector，which is normalized as：H is a m×n dimension observation matrix．Rank［H］＝n．Its elements are decided by the structureof power system and the configuration of meteringsystem．In general case，H can act as constant be－cause its change is minute in every iteration．
　　The observation function applying weightedleastsquares algorithmis：

where R－1 is weight，Δz is the difference betweenthe measurementand the value ofthe correspondingmeasurementfunction．Eq．（2）is expressed in a vec－
tor form：

3　THEREALIZATION OFRLSALGO-RITHM USINGANEURALNETWORK
    According to the reference[3]that a energy function was used to research the stability of a feed－back neuralnetwork and simulation electroniccircuitcould realize its circuitmodel．In reference［1］，thereis a network that comprised of a main and a sub－sidiary network，showed as the Figure 1．The paperapplies the network to power system state estima－tion successfully．The main and the subsidiary neu－rons are connected with each other．The left mainnetwork has n neurons，every neuron is modeled asan amplifier，and the relation ofitsinput and outputis nonlinear．It has input capacitance Ci and resis－tance Ri．vi（t）and ui（t）are the i－th neuron output and input voltage．g（u）is a degressive function．

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