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《Power System Protection and Control》 2010-17
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with adjacent incremental algorithm of transformer fault diagnosis

LIU Tong-jie1,2,LIU Zhi-gang1,HAN Zhi-wei1 (1. School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China; 2. Leshan Electricity Power Bureau,Leshan 614000,China)  
A new method of transformer fault diagnosis based on adaptive fuzzy support vector machine with adjacent incremental algorithm is proposed. Based on the concept of feature extraction in pattern recognition,a hierarchical structure is employed to extract the input features closely related to the model of classification,and it has effectively suppressed the interference of redundant information; the adaptive parameter optimization algorithm has both increased the flexibility of parameter selection for FSVM and enhanced the convergence speed. Besides,the adjacent incremental algorithm has both improved the precision of the diagnosis model and increased the learning abilities to new samples. Compared with the diagnosis model of multi-SVM and multi-FSVM,this method has a strong convergence and excellent diagnosis result.
【Fund】: 2008教育部新世纪优秀人才支持项目(NECT-08-0825);; 教育部霍英东青年教师基金(101060);; 四川省杰出青年基金(07ZQ026-012)
【CateGory Index】: TM407
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