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
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/20/4017 |
| _version_ | 1817990860802883584 |
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| author | Haikun Shang Junyan Xu Zitao Zheng Bing Qi Liwei Zhang |
| author_facet | Haikun Shang Junyan Xu Zitao Zheng Bing Qi Liwei Zhang |
| author_sort | Haikun Shang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T01:04:37Z |
| format | Article |
| id | doaj.art-9ba85e389fb94d3aa5aa5c32ad943717 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:04:37Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9ba85e389fb94d3aa5aa5c32ad9437172022-12-22T02:21:17ZengMDPI AGEnergies1996-10732019-10-011220401710.3390/en12204017en12204017A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence TheoryHaikun Shang0Junyan Xu1Zitao Zheng2Bing Qi3Liwei Zhang4College of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaState Grid Zhangjiakou Power Supply Company, Zhangjiakou 075000, ChinaCollege of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaPower 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.https://www.mdpi.com/1996-1073/12/20/4017power transformerdissolved gas analysisfault diagnosishmsvmd–s evidence theory |
| spellingShingle | Haikun Shang Junyan Xu Zitao Zheng Bing Qi Liwei Zhang 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 power transformer dissolved gas analysis fault diagnosis hmsvm d–s evidence theory |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | novel fault diagnosis method for power transformer based on dissolved gas analysis using hypersphere multiclass support vector machine and improved d s evidence theory |
| topic | power transformer dissolved gas analysis fault diagnosis hmsvm d–s evidence theory |
| url | https://www.mdpi.com/1996-1073/12/20/4017 |
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