Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering
The traditional method of detecting fault current based on threshold judgment method is limited by the current size and is easily disturbed by noise, and it is difficult to adapt to the arc ground fault detection of the distribution network. Aiming at this problem, this paper proposes a single-phase...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1036984/full |
| _version_ | 1797956581639323648 |
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| author | Yihui Wu Qiong Li Guohua Long Liangliang Chen Muliang Cai Wenbao Wu |
| author_facet | Yihui Wu Qiong Li Guohua Long Liangliang Chen Muliang Cai Wenbao Wu |
| author_sort | Yihui Wu |
| collection | DOAJ |
| description | The traditional method of detecting fault current based on threshold judgment method is limited by the current size and is easily disturbed by noise, and it is difficult to adapt to the arc ground fault detection of the distribution network. Aiming at this problem, this paper proposes a single-phase arc-optic ground fault identification method based on waveform subsequence splitting fault segmentation, combined with three-phase voltage-zero sequence voltage waveform feature extraction clustering. First of all, the waveform fault segment is segmented and located, secondly, the characteristic indexes of the time domain and frequency domain of the combined three-phase voltage-zero sequence voltage waveform are established, and the multidimensional feature distribution is reduced by the principal component analysis method, and finally, the characteristic distribution after the dimensionality reduction is identified by the K-means clustering algorithm based on the waveform subsequence. Experimental results show that the arc light grounding fault identification method proposed in this paper achieves 97.12% accurate identification of the test sample. |
| first_indexed | 2024-04-10T23:51:05Z |
| format | Article |
| id | doaj.art-2b7113fbc1464252bc761f363a8e3c48 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T23:51:05Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-2b7113fbc1464252bc761f363a8e3c482023-01-10T17:39:04ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.10369841036984Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clusteringYihui Wu0Qiong Li1Guohua Long2Liangliang Chen3Muliang Cai4Wenbao Wu5Nanchang Hangkong University, Nanchang, ChinaNanchang Hangkong University, Nanchang, ChinaNational Network Jiangxi Electric Power Co. Ltd., Nanchang, ChinaNanchang Hangkong University, Nanchang, ChinaNational Network Jiangxi Electric Power Co. Ltd., Nanchang, ChinaChina Power Construction Group Jiangxi Electric Power Construction Co. Ltd., Nanchang, ChinaThe traditional method of detecting fault current based on threshold judgment method is limited by the current size and is easily disturbed by noise, and it is difficult to adapt to the arc ground fault detection of the distribution network. Aiming at this problem, this paper proposes a single-phase arc-optic ground fault identification method based on waveform subsequence splitting fault segmentation, combined with three-phase voltage-zero sequence voltage waveform feature extraction clustering. First of all, the waveform fault segment is segmented and located, secondly, the characteristic indexes of the time domain and frequency domain of the combined three-phase voltage-zero sequence voltage waveform are established, and the multidimensional feature distribution is reduced by the principal component analysis method, and finally, the characteristic distribution after the dimensionality reduction is identified by the K-means clustering algorithm based on the waveform subsequence. Experimental results show that the arc light grounding fault identification method proposed in this paper achieves 97.12% accurate identification of the test sample.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1036984/fullarc groundingwaveform subsequencesK-means clusteringfault identificationsequence segmentation |
| spellingShingle | Yihui Wu Qiong Li Guohua Long Liangliang Chen Muliang Cai Wenbao Wu Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering Frontiers in Energy Research arc grounding waveform subsequences K-means clustering fault identification sequence segmentation |
| title | Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering |
| title_full | Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering |
| title_fullStr | Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering |
| title_full_unstemmed | Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering |
| title_short | Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering |
| title_sort | research on arc grounding identification method of distribution network based on waveform subsequence segmentation clustering |
| topic | arc grounding waveform subsequences K-means clustering fault identification sequence segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1036984/full |
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