Abstract：To overcome the problem of inherent binary-class nature and incomplete information for traditional single kernel classifier,a novel method based on multi-kernel multi-class relevance vector machine(MMRVM) is proposed for partial discharge pattern recognition. Firstly,4 different partial discharge signal features are mapped with different kernel functions to solve the problem of different data sources. Secondly,particle swarm optimization is applied for kernel parameters selection to avoid the subjectivity of parameter selection. Finally pattern recognition of partial discharge is realized by direct multi-classification with constructed model. 4 types of partial discharge signals simulated in the laboratory are analyzed with traditional single kernel SVM,RVM and MMRVM classifier effectively. Experiment results demonstrate that,MMRVM classifier combines with various kinds of partial discharge features that can comprehensively describe partial discharge features. The proposed method has higher recognition accuracy and better applicability than single kernel classifiers.
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2014, Vol. 29 
