ArticleEntropy (Basel, Switzerland)2019
Partial Discharge Fault Diagnosis Based on Multi-Scale Dispersion Entropy and a Hypersphere Multiclass Support Vector Machine.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Major Role of Multiscale Entropy Evolution in Complex Systems and Data Science.Entropy (Basel, Switzerland) · 2024Article
- PV System Failures Diagnosis Based on Multiscale Dispersion Entropy.Entropy (Basel, Switzerland) · 2022Article
- Fault Diagnosis of Power Transformer Based on Time-Shift Multiscale Bubble Entropy and Stochastic Configuration Network.Entropy (Basel, Switzerland) · 2022Article
- Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.Entropy (Basel, Switzerland) · 2021Article
- Identification of Denatured Biological Tissues Based on Compressed Sensing and Improved Multiscale Dispersion Entropy during HIFU Treatment.Entropy (Basel, Switzerland) · 2020Article
- Multiscale Entropy Approaches and Their Applications.Entropy (Basel, Switzerland) · 2020Article
- Evaluating Signalization and Channelization Selections at Intersections Based on an Entropy Method.Entropy (Basel, Switzerland) · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
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.
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