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《Proceedings of the Csee》 2005-20
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STUDY ON KERNEL-BASED POSSIBILISTIC CLUSTERING AND DISSOLVED GAS ANALYSIS FOR FAULT DIAGNOSIS OF POWER TRANSFORMER

XIONG Hao,SUN Cai-xin,LIAO Rui-jin,LI Jian,DU Lin(Key Laboratory of High Voltage and Electrical New Technology of Ministry of Education,Chongqing University,Shapingba District,Chongqing 400044,China)  
Dissolved gas analysis (DGA) is an important method to diagnose the fault of power transformer.Aimed at the problems existed in fuzzy c-means clustering algorithm which is applied in DGA,a simplified kernel-based possibilistic c-means clustering algorithm is presented.This algorithm combines kernel function with possibilistic c-means clustering,then is applied to DGA data analysis in power transformer.It's proved that this algorithm can cluster the samples fast and efficiently,and adapts to the environment of containing the noise samples.
【CateGory Index】: TM407
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