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

Haikun Shang, Junyan Xu, Zitao Zheng, Bing Qi and Liwei Zhang
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Haikun Shang: College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Junyan Xu: College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Zitao Zheng: State Grid Zhangjiakou Power Supply Company, Zhangjiakou 075000, China
Bing Qi: College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Liwei Zhang: College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2019, vol. 12, issue 20, 1-22

Abstract: 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.

Keywords: power transformer; dissolved gas analysis; fault diagnosis; HMSVM; D–S evidence theory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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