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《Journal of System Simulation》 2014-06
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Fault Diagnosis Model of Transformer Based on BP Neural Network

YU Jian-li;BIAN Shuai;Zhengzhou Institute of Aeronautical Industry Managemen;  
According to the characteristics of fault types of the transformer, BP neural network was used to diagnose transformer fault. Six gases were regarded as inputs of the neural network and established BP neural network model which could diagnose seven transformer faults: low discharge, high energy discharge, partial discharge, low temperature heat, medium temperature overheat, high temperature overheat, high temperature overheat and mixed fault of high temperature overhead and high energy discharge. By adjusting the number of hidden layer neurons, the network was trained to optimize the network structure and parameters. The output of the neural network was processed according to the maximum subordination principle. Simulation studies show that the rate of the transformer fault diagnosis accuracy reaches95%. Additionally, the BP neural network model is easy to establish and applicable to use.
【Fund】: 河南省自然科学基金项目(132102210091 142102210077 142102210105)
【CateGory Index】: TP183;TP206.3
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