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《High Voltage Engineering》 2004-05
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APPLICATION OF FUZZY NEURAL NETWORK TO TRANSFORMER FAULT DIAGNOSIS

Peng Ningyun, Wen Xishan, Shu Xiang (Electrical Engineering Institute of Whuhan University, Wuhan 430072, China)  
Based on fuzzy neural network, a new method of power transformer fault diagnosis is proposed in this paper. The method combines fuzzy diagnosis with neural network, which solves the difficulty of fuzzy diagnosis in self-studying and finds out the physical meaning of the weight matrix with the help of the relation between the neural network weight matrix and the fuzzy decision matrix. That is to say the neural network weight matrix is the fuzzy decision matrix. The respective accuracies of fuzzy neural network, combinatorial neural network and decision tree is 90.4%, 75.4% or 83.3%. The results have validated that the correctness, the validity and feasibility of the method. The contrast analysis has pointed out the advantages of the proposed method, compared to combinatorial neural network and decision tree. It has offset the insufficiency of DGA test, overcome the limitation of combinatorial neural network which possesses no plasticity, avoided the intensive dependence of decision tree to samples, and increased the accuracy of fault diagnosis greatly. It also explained the necessity of synthetic analysis that DGA test is combined with other electrical tests such as absorptance test, dielectric loss test and so on.
【CateGory Index】: TM41
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