Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine

被引:22
|
作者
Shang, Haikun [1 ]
Li, Feng [2 ]
Wu, Yingjie [3 ]
机构
[1] Northeast Elect Power Univ, Coll Elect Engn, Jilin 132012, Jilin, Peoples R China
[2] State Grid Elect Power Res Inst, Xinjiang 830011, Peoples R China
[3] Northeast Elect Power Univ, Coll Automat Engn, Jilin 132012, Jilin, Peoples R China
关键词
PD; fault diagnosis; variational mode decomposition; multi-scale dispersion entropy; HMSVM; APPROXIMATE ENTROPY; WAVELET TRANSFORM; NETWORK;
D O I
10.3390/e21010081
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Partial discharge (PD) fault analysis is an important tool for insulation condition diagnosis of electrical equipment. In order to conquer the limitations of traditional PD fault diagnosis, a novel feature extraction approach based on variational mode decomposition (VMD) and multi-scale dispersion entropy (MDE) is proposed. Besides, a hypersphere multiclass support vector machine (HMSVM) is used for PD pattern recognition with extracted PD features. Firstly, the original PD signal is decomposed with VMD to obtain intrinsic mode functions (IMFs). Secondly proper IMFs are selected according to central frequency observation and MDE values in each IMF are calculated. And then principal component analysis (PCA) is introduced to extract effective principle components in MDE. Finally, the extracted principle factors are used as PD features and sent to HMSVM classifier. Experiment results demonstrate that, PD feature extraction method based on VMD-MDE can extract effective characteristic parameters that representing dominant PD features. Recognition results verify the effectiveness and superiority of the proposed PD fault diagnosis method.
引用
收藏
页数:19
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