Multi-feature based extreme learning machine identification model of incipient cable faults
In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into p...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1364528/full |
| _version_ | 1797215213754253312 |
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| author | Feng Wang Pengping Zhang Jianxiu Li Zhiqi Li Mingzhe Zhao Yongliang Liang Guoqiang Su Xinhong You |
| author_facet | Feng Wang Pengping Zhang Jianxiu Li Zhiqi Li Mingzhe Zhao Yongliang Liang Guoqiang Su Xinhong You |
| author_sort | Feng Wang |
| collection | DOAJ |
| description | In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into permanent failures. To address this issue, this paper analyzes the development mechanism and characteristics of latent cable faults. A 10kV low-resistance cable latent fault model based on the Kizilcay arc model is built in the PSCAD/EMTDC platform. Furthermore, the paper analyzes and extracts the time-domain, frequency-domain, and time-frequency domain features of fault current samples. Effective fault feature vectors are constructed using multivariate analysis of variance (MANOVA) and Principal Component Analysis (PCA). Based on the fault feature vectors and Extreme Learning Machine (ELM), an intelligent fault identification model for cable latent faults is developed. The initial parameters of the ELM model are optimized using the Particle Swarm Optimization (PSO) algorithm. Finally, the superiority of the proposed model is validated in terms of classification accuracy, training time, and robustness compared to other machine learning algorithms. |
| first_indexed | 2024-04-24T11:26:30Z |
| format | Article |
| id | doaj.art-371a640267db415d876d9ca42a574f33 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-24T11:26:30Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-371a640267db415d876d9ca42a574f332024-04-10T14:52:31ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-04-011210.3389/fenrg.2024.13645281364528Multi-feature based extreme learning machine identification model of incipient cable faultsFeng Wang0Pengping Zhang1Jianxiu Li2Zhiqi Li3Mingzhe Zhao4Yongliang Liang5Guoqiang Su6Xinhong You7State Grid Shandong Electric Power Research Institute, Jinan, ChinaState Grid Shandong Electric Power Research Institute, Jinan, ChinaState Grid Shandong Electric Power Company, Jinan, ChinaSchool of Electrical Engineer, Shandong University, Jinan, ChinaState Grid Shandong Electric Power Research Institute, Jinan, ChinaSchool of Electrical Engineer, Shandong University, Jinan, ChinaState Grid Shandong Electric Power Research Institute, Jinan, ChinaState Grid Shandong Electric Power Research Institute, Jinan, ChinaIn the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into permanent failures. To address this issue, this paper analyzes the development mechanism and characteristics of latent cable faults. A 10kV low-resistance cable latent fault model based on the Kizilcay arc model is built in the PSCAD/EMTDC platform. Furthermore, the paper analyzes and extracts the time-domain, frequency-domain, and time-frequency domain features of fault current samples. Effective fault feature vectors are constructed using multivariate analysis of variance (MANOVA) and Principal Component Analysis (PCA). Based on the fault feature vectors and Extreme Learning Machine (ELM), an intelligent fault identification model for cable latent faults is developed. The initial parameters of the ELM model are optimized using the Particle Swarm Optimization (PSO) algorithm. Finally, the superiority of the proposed model is validated in terms of classification accuracy, training time, and robustness compared to other machine learning algorithms.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1364528/fullcable incipient faultfeature extractiondata-drivenextreme learning machineparticle swarm optimization |
| spellingShingle | Feng Wang Pengping Zhang Jianxiu Li Zhiqi Li Mingzhe Zhao Yongliang Liang Guoqiang Su Xinhong You Multi-feature based extreme learning machine identification model of incipient cable faults Frontiers in Energy Research cable incipient fault feature extraction data-driven extreme learning machine particle swarm optimization |
| title | Multi-feature based extreme learning machine identification model of incipient cable faults |
| title_full | Multi-feature based extreme learning machine identification model of incipient cable faults |
| title_fullStr | Multi-feature based extreme learning machine identification model of incipient cable faults |
| title_full_unstemmed | Multi-feature based extreme learning machine identification model of incipient cable faults |
| title_short | Multi-feature based extreme learning machine identification model of incipient cable faults |
| title_sort | multi feature based extreme learning machine identification model of incipient cable faults |
| topic | cable incipient fault feature extraction data-driven extreme learning machine particle swarm optimization |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1364528/full |
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