Transformer fault classification for diagnosis based on DGA and deep belief network
Power transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dis...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723014294 |
| _version_ | 1797374165870706688 |
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| author | Dexu Zou Zixiong Li Hao Quan Qingjun Peng Shan Wang Zhihu Hong Weiju Dai Tao Zhou Jianhua Yin |
| author_facet | Dexu Zou Zixiong Li Hao Quan Qingjun Peng Shan Wang Zhihu Hong Weiju Dai Tao Zhou Jianhua Yin |
| author_sort | Dexu Zou |
| collection | DOAJ |
| description | Power transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dissolved gas analysis (DGA) technology is used to identify fault types through dissolved gas in transformer oil, and its reliability has been proved. In order to analyze these gases and diagnose transformer fault types with the results, many methods have been developed, such as Key Gas Method, Method of Duval, IEC 60599 Method, Method of Dornenburg and Method of Rogers, etc. In some cases, the accuracy of these traditional methods is reduced and cannot be applied for diagnosis, since they have fixed input features and is not flexible for input combination. In order to achieve the propose of solving this defect, this paper introduces a deep belief network-based DGA method to diagnose the faults and states of power transformers with customized input features. For this work, six fault classifications were considered based on the nine characteristics extracted from the gases precipitated from the insulating oil of power transformers. The deep belief network was tested using oil samples collected from power transformers. Experiments have shown that the performance of the network has obtained relatively good accuracy results. |
| first_indexed | 2024-03-08T19:01:54Z |
| format | Article |
| id | doaj.art-886810ee84da45d1be1013817c689bc0 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T19:01:54Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-886810ee84da45d1be1013817c689bc02023-12-28T05:18:16ZengElsevierEnergy Reports2352-48472023-11-019250256Transformer fault classification for diagnosis based on DGA and deep belief networkDexu Zou0Zixiong Li1Hao Quan2Qingjun Peng3Shan Wang4Zhihu Hong5Weiju Dai6Tao Zhou7Jianhua Yin8Electric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, China; Corresponding author.Electric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaElectric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaElectric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaElectric Power Research Institute, China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, ChinaPower transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dissolved gas analysis (DGA) technology is used to identify fault types through dissolved gas in transformer oil, and its reliability has been proved. In order to analyze these gases and diagnose transformer fault types with the results, many methods have been developed, such as Key Gas Method, Method of Duval, IEC 60599 Method, Method of Dornenburg and Method of Rogers, etc. In some cases, the accuracy of these traditional methods is reduced and cannot be applied for diagnosis, since they have fixed input features and is not flexible for input combination. In order to achieve the propose of solving this defect, this paper introduces a deep belief network-based DGA method to diagnose the faults and states of power transformers with customized input features. For this work, six fault classifications were considered based on the nine characteristics extracted from the gases precipitated from the insulating oil of power transformers. The deep belief network was tested using oil samples collected from power transformers. Experiments have shown that the performance of the network has obtained relatively good accuracy results.http://www.sciencedirect.com/science/article/pii/S2352484723014294Power transformerFault diagnosisDissolved gas analysisDeep belief network |
| spellingShingle | Dexu Zou Zixiong Li Hao Quan Qingjun Peng Shan Wang Zhihu Hong Weiju Dai Tao Zhou Jianhua Yin Transformer fault classification for diagnosis based on DGA and deep belief network Energy Reports Power transformer Fault diagnosis Dissolved gas analysis Deep belief network |
| title | Transformer fault classification for diagnosis based on DGA and deep belief network |
| title_full | Transformer fault classification for diagnosis based on DGA and deep belief network |
| title_fullStr | Transformer fault classification for diagnosis based on DGA and deep belief network |
| title_full_unstemmed | Transformer fault classification for diagnosis based on DGA and deep belief network |
| title_short | Transformer fault classification for diagnosis based on DGA and deep belief network |
| title_sort | transformer fault classification for diagnosis based on dga and deep belief network |
| topic | Power transformer Fault diagnosis Dissolved gas analysis Deep belief network |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723014294 |
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