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《Journal of Beijing Jiaotong University》 2012-01
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Research of transformer fault diagnosis based on fuzzy support vector machines

XIAO Yancai,ZHANG Qing(School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Beijing 100044,China)  
As the traditional support vector machines(SVM) is particularly sensitive to noise and outliers in the training samples,transformer fault diagnosis model is built based on fuzzy support vector machines(FSVM).It selects binary tree classification algorithm for multi-class classification.The membership value of the FSVM is obtained by fuzzy C-means clustering algorithm.The radial kernel is selected.Parameters of the FSVM model are optimized with genetic algorithm.A mass of fault samples are analyzed and results are compared with those obtained by the methods of BPNN and SVM,which shows that the proposed model is more effective and accurate,which proves the given method of transformer fault diagnosis based on binary tree fuzzy support vector machines is feasible.
【Fund】: 中央高校基本科研业务费专项资金资助(2011JBM092)
【CateGory Index】: TM407
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