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《High Voltage Engineering》 2006-08
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Application of Nguyen-widrow Method in Transformer Dissolved Gas-in-oil Analysis Fault Diagnosis

XU Zhiniu,L Fangcheng (Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control Under Ministry of Education, North China Electric Power University, Baoding 071003, China)  
For solving the problem of slow convergence speed of ANN used in transformer fault diagnosis based on dissolved gas-in-oil analysis (DGA), Nguyen-Widrow method is used to initialize the variable parameters of ANN. The weight value and bias value of ANN are adjusted by the method to make neurons of hidden layer have equal linear interval, so the computation quantity of ANN is reduced, and the convergence speed of ANN is increased, learn capacity and fault diagnosis accuracy of ANN is improved. A neural network take 5-80-6 as architecture, and 393 samples are used to train, and 155 samples are used to verify effect of training is build. The method of Nguyen-Widrow makes the training error sum of square of ANN from 37.1 down to 15.21, and make the diagnosis accuracy of ANN from 80.97% improved to 84.49%. The effect of the method is verified by the training result and diagnosis reuslt of ANN for transformer fault diagnosis.
【CateGory Index】: TM407
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