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Article

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

by 1,*, 2 and 3
1
College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
2
State Grid Electric Power Research Institute, Xinjiang 830011, China
3
College of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(1), 81; https://doi.org/10.3390/e21010081
Received: 24 December 2018 / Revised: 9 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
(This article belongs to the Special Issue Multiscale Entropy Approaches and Their Applications)
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. View Full-Text
Keywords: PD; fault diagnosis; variational mode decomposition; multi-scale dispersion entropy; HMSVM PD; fault diagnosis; variational mode decomposition; multi-scale dispersion entropy; HMSVM
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MDPI and ACS Style

Shang, H.; Li, F.; Wu, Y. Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine. Entropy 2019, 21, 81. https://doi.org/10.3390/e21010081

AMA Style

Shang H, Li F, Wu Y. Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine. Entropy. 2019; 21(1):81. https://doi.org/10.3390/e21010081

Chicago/Turabian Style

Shang, Haikun; Li, Feng; Wu, Yingjie. 2019. "Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine" Entropy 21, no. 1: 81. https://doi.org/10.3390/e21010081

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