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Open AccessArticle

A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

1
College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
2
State Grid Zhangjiakou Power Supply Company, Zhangjiakou 075000, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(20), 4017; https://doi.org/10.3390/en12204017
Received: 30 August 2019 / Revised: 18 October 2019 / Accepted: 18 October 2019 / Published: 22 October 2019
(This article belongs to the Special Issue Power Transformer Condition Assessment)
Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy. View Full-Text
Keywords: power transformer; dissolved gas analysis; fault diagnosis; HMSVM; D–S evidence theory power transformer; dissolved gas analysis; fault diagnosis; HMSVM; D–S evidence theory
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MDPI and ACS Style

Shang, H.; Xu, J.; Zheng, Z.; Qi, B.; Zhang, L. A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory. Energies 2019, 12, 4017. https://doi.org/10.3390/en12204017

AMA Style

Shang H, Xu J, Zheng Z, Qi B, Zhang L. A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory. Energies. 2019; 12(20):4017. https://doi.org/10.3390/en12204017

Chicago/Turabian Style

Shang, Haikun; Xu, Junyan; Zheng, Zitao; Qi, Bing; Zhang, Liwei. 2019. "A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory" Energies 12, no. 20: 4017. https://doi.org/10.3390/en12204017

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