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《High Voltage Engineering》 2008-11
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Power Transformer Fault Diagnosis Based on Neural Network Evolved by Particle Swarm Optimization

WANG Xiao-xia1,WANG Tao2(1. School of Computer Science & Technology,North China Electric Power University,Baoding 071003,China; 2. School of Mathematics & Physics,North China Electric Power University,Baoding 071003,China)  
Power transformer is one of the most important equipments in power network. Its normal operation is the basis of the power supply and normal social life. So it is valuable to discover the incipient fault accurately and timely. This paper proposes a new power transformer fault diagnostic method using neural network evolved by modified particle swarm optimization (modified PSO) algorithm in order to overcome the problem of premature convergence observed in many applications of error back propagation (BP) algorithm and enhance the fault diagnostic ability of conventional dissolved gas-in-oil analysis in power transformer. In the modified PSO algorithm,the inertia weight is adjusted adaptively in order to balance and reconcile the global and local searching capability. The convergence can be accelerated by setting the compression factor of the modified PSO algorithm reasonably,which may benefit to find the global optimal solution quickly. Firstly,the modified PSO algorithm is used to optimize the original parameter of the neural network,then the gradient descent algorithm is used to train the neural network. Defects of conventional BP algorithm,i.e. the slow convergence of weight and threshold learning,premature result,and the slow training speed of GA,are settled by the algorithm. Finally,Simulation results of power transformer fault diagnosis show that both convergence speed and diagnosis accuracy are improved to some extent. That shows the correctness and validity of this method in power transformer fault diagnosis by dissolved gas-in-oil analysis.
【Fund】: 国家自然科学基金(10774043)~~
【CateGory Index】: TM41;TP183
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