Transmission line fault-cause identification method for large-scale new energy grid connection scenarios
The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines. With the goal of achieving “carbon peak and carbon neutrality”, the schemes for...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-08-01
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| Series: | Global Energy Interconnection |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096511722000767 |
| _version_ | 1811211511728701440 |
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| author | Hanqing Liang Xiaonan Han Haoyang Yu Fan Li Zhongjian Liu Kexin Zhang |
| author_facet | Hanqing Liang Xiaonan Han Haoyang Yu Fan Li Zhongjian Liu Kexin Zhang |
| author_sort | Hanqing Liang |
| collection | DOAJ |
| description | The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines. With the goal of achieving “carbon peak and carbon neutrality”, the schemes for clean energy generation have rapidly developed. Moreover, new energy-consuming equipment has been widely connected to the power grid, and the operating characteristics of the power system have significantly changed. Consequently, these have impacted traditional fault identification methods. Based on the time-frequency characteristics of the fault waveform, new energy-related parameters, and deep learning model, this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid. Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters. Further, a fault identification model based on adaptive deep belief networks was constructed, and its effect was verified by field data. |
| first_indexed | 2024-04-12T05:14:22Z |
| format | Article |
| id | doaj.art-f8b822767b3d435eb3f302059bec7028 |
| institution | Directory Open Access Journal |
| issn | 2096-5117 |
| language | English |
| last_indexed | 2024-04-12T05:14:22Z |
| publishDate | 2022-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Global Energy Interconnection |
| spelling | doaj.art-f8b822767b3d435eb3f302059bec70282022-12-22T03:46:41ZengKeAi Communications Co., Ltd.Global Energy Interconnection2096-51172022-08-0154362374Transmission line fault-cause identification method for large-scale new energy grid connection scenariosHanqing Liang0Xiaonan Han1Haoyang Yu2Fan Li3Zhongjian Liu4Kexin Zhang5State Power Economic and Technological Research Institute Co, Ltd, Beijing 102206, PR ChinaState Power Economic and Technological Research Institute Co, Ltd, Beijing 102206, PR ChinaState Power Economic and Technological Research Institute Co, Ltd, Beijing 102206, PR ChinaState Power Economic and Technological Research Institute Co, Ltd, Beijing 102206, PR ChinaState Power Economic and Technological Research Institute Co, Ltd, Beijing 102206, PR ChinaState Power Economic and Technological Research Institute Co, Ltd, Beijing 102206, PR ChinaThe accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines. With the goal of achieving “carbon peak and carbon neutrality”, the schemes for clean energy generation have rapidly developed. Moreover, new energy-consuming equipment has been widely connected to the power grid, and the operating characteristics of the power system have significantly changed. Consequently, these have impacted traditional fault identification methods. Based on the time-frequency characteristics of the fault waveform, new energy-related parameters, and deep learning model, this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid. Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters. Further, a fault identification model based on adaptive deep belief networks was constructed, and its effect was verified by field data.http://www.sciencedirect.com/science/article/pii/S2096511722000767Fault-cause identificationTransmission linesFault waveformLarge-scale new energyFault cause |
| spellingShingle | Hanqing Liang Xiaonan Han Haoyang Yu Fan Li Zhongjian Liu Kexin Zhang Transmission line fault-cause identification method for large-scale new energy grid connection scenarios Global Energy Interconnection Fault-cause identification Transmission lines Fault waveform Large-scale new energy Fault cause |
| title | Transmission line fault-cause identification method for large-scale new energy grid connection scenarios |
| title_full | Transmission line fault-cause identification method for large-scale new energy grid connection scenarios |
| title_fullStr | Transmission line fault-cause identification method for large-scale new energy grid connection scenarios |
| title_full_unstemmed | Transmission line fault-cause identification method for large-scale new energy grid connection scenarios |
| title_short | Transmission line fault-cause identification method for large-scale new energy grid connection scenarios |
| title_sort | transmission line fault cause identification method for large scale new energy grid connection scenarios |
| topic | Fault-cause identification Transmission lines Fault waveform Large-scale new energy Fault cause |
| url | http://www.sciencedirect.com/science/article/pii/S2096511722000767 |
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