Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network
To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2024-03-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-3-295.shtml |
| _version_ | 1797237755992866816 |
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| author | CHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie |
| author_facet | CHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie |
| author_sort | CHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie |
| collection | DOAJ |
| description | To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems. |
| first_indexed | 2024-04-24T17:24:48Z |
| format | Article |
| id | doaj.art-57cb8121cdfb4f8a9bd5c5bd9785424b |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-04-24T17:24:48Z |
| publishDate | 2024-03-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-57cb8121cdfb4f8a9bd5c5bd9785424b2024-03-28T07:32:43ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672024-03-0158329530310.16183/j.cnki.jsjtu.2022.091Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention NetworkCHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie01. School of Electronic Information and Electric Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. State Grid Qinghai Electric Power Company, Xining 810008, China;3. State Grid Beijing Electric Power Company, Beijing 100031, ChinaTo improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems.https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-3-295.shtmlpower distribution systemsfaults identificationattention mechanismgated recurrent units (gru) |
| spellingShingle | CHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network Shanghai Jiaotong Daxue xuebao power distribution systems faults identification attention mechanism gated recurrent units (gru) |
| title | Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network |
| title_full | Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network |
| title_fullStr | Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network |
| title_full_unstemmed | Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network |
| title_short | Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network |
| title_sort | fault detection in power distribution systems based on gated recurrent attention network |
| topic | power distribution systems faults identification attention mechanism gated recurrent units (gru) |
| url | https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-3-295.shtml |
| work_keys_str_mv | AT chenhaolanjinbingyingliuyadongqianqinglinwangpengchenyanxiayuxijuanyanyingjie faultdetectioninpowerdistributionsystemsbasedongatedrecurrentattentionnetwork |