基于主成分分析和多分类相关向量机的GIS局部放电模式识别

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Abstract GIS partial discharge pattern recognition is an important part of its state evaluation, and a 252kV GIS partial discharge detection simulation experiment platform based on UHF method, and four kinds of typical partial discharge models in laboratory are designed, then corresponding UHF signal mapping database through the experimental method, and also extracted the original 26 feature parameters; the authors reduced the dimension of the feature space through principal component analysis, and then obtain 10 new characteristics parameters, inputed the original features and the characteristic parameters after dimension reduction to the classification of relevance vector machine classifier respectively. The results show that taking the characteristic parameters after the dimension reduction as input, the recognition rate is higher than the original feature parameters; BN, SVM adopt to do the comparison with milticlass relevance vector machine, The results show that input the original characteristic parameters or the dimension reduction parameters, the M-RVM method recognition rate is highest, and the recognition rate is above 85% after the dimension reduction.

Key words： Gas insulated switchgear(GIS) partial discharge(PD) principal component analysis milticlass relevance vector machine(M-RVM) pattern recognition

Received: 02 January 2014 Published: 10 April 2015

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