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《High Voltage Engineering》 2010-02
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Power Transformer Fault Diagnosis Using Weighted Fuzzy Kernel Clustering

FU Yang,TIAN Zhen-ning,JIANG Yu-rong,CAO Jia-lin(Shanghai University of Electric Power,Shanghai 200090,China)  
Dissolved gas analysis(DGA)is an important method to diagnose the fault of power transformer.To solve the problems existed in fuzzy c-means clustering algorithm which is applied in DGA,the weighted fuzzy kernel clustering(WFKC) algorithm is introduced into the fault diagnosis of power transformers to build a new fault diagnosis model.In the algorithm,firstly considering that the different effects of the different attributes on cluster results,so the similarity based weighting method is used to assign weight to features of the transferred samples,and then weighted fuzzy kernel clustering in the feature space is realized when the transferred samples in the original space is mapped into high-dimensional feature space.The new fault diagnosis model can adequately consider that the different effects of the different attributes on cluster results and effectively improve the clustering capability for the complex dataset,and the correct rate is effectively improved.WFKC is applied in practice to analyze fault diagnosis of power transformers,The results demonstrate that this algorithm can cluster the samples fast and efficiently,and WFKC is feasible and valid.
【Fund】: 上海市重大科技攻关计划项目(08dz1200600);; 上海市教委重点学科建设项目(J51303)~~
【CateGory Index】: TM41
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