A real-time topology identification method of distribution networks based on CNN-LSTM-Attention
Accurate identification of the topology in a distribution network is crucial for its operation and control. Addressing the dynamic changes in the actual topology of distribution networks， an intelligent deep learning model capable of recognizing distribution network topologies was developed. Firstly...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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zhejiang electric power
2024-03-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=3d2aff3a-187d-49d6-8d55-eda22b006f15 |
| _version_ | 1827300327230013440 |
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| author | LING Jiakai ZHANG Yizhou HU Jinfeng QIN Jun DAI Jian FEI Youdie ZHU Zhen |
| author_facet | LING Jiakai ZHANG Yizhou HU Jinfeng QIN Jun DAI Jian FEI Youdie ZHU Zhen |
| author_sort | LING Jiakai |
| collection | DOAJ |
| description | Accurate identification of the topology in a distribution network is crucial for its operation and control. Addressing the dynamic changes in the actual topology of distribution networks， an intelligent deep learning model capable of recognizing distribution network topologies was developed. Firstly， measurement data for distribution networks under different topologies were generated， followed by data preprocessing. Subsequently， an intelligent topology identification model was constructed， integrating convolutional neural network （CNN）， long short-term memory network （LSTM）， and Attention mechanism. The model was trained and tested using historical measurement data. Finally， in simulation scenarios using the IEEE 33-node and PG&E69-node distribution systems， the superiority of this CNN-LSTM-Attention-based topology identification method over traditional approaches in terms of identification accuracy was validated， and online application of the model was achieved. |
| first_indexed | 2024-04-24T16:01:41Z |
| format | Article |
| id | doaj.art-6ab8b26e9b5c480295bb3391769e5417 |
| institution | Directory Open Access Journal |
| issn | 1007-1881 |
| language | zho |
| last_indexed | 2024-04-24T16:01:41Z |
| publishDate | 2024-03-01 |
| publisher | zhejiang electric power |
| record_format | Article |
| series | Zhejiang dianli |
| spelling | doaj.art-6ab8b26e9b5c480295bb3391769e54172024-04-01T07:39:02Zzhozhejiang electric powerZhejiang dianli1007-18812024-03-01433849410.19585/j.zjdl.2024030101007-1881（2024）03-0084-11A real-time topology identification method of distribution networks based on CNN-LSTM-AttentionLING Jiakai0ZHANG Yizhou1HU Jinfeng2QIN Jun3DAI Jian4FEI Youdie5ZHU Zhen6Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi Jiangsu 214061, ChinaSchool of Energy and Electrical and Power Engineering, Hohai University, Nanjing 211100, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi Jiangsu 214061, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi Jiangsu 214061, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi Jiangsu 214061, ChinaSchool of Energy and Electrical and Power Engineering, Hohai University, Nanjing 211100, ChinaWuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi Jiangsu 214061, ChinaAccurate identification of the topology in a distribution network is crucial for its operation and control. Addressing the dynamic changes in the actual topology of distribution networks， an intelligent deep learning model capable of recognizing distribution network topologies was developed. Firstly， measurement data for distribution networks under different topologies were generated， followed by data preprocessing. Subsequently， an intelligent topology identification model was constructed， integrating convolutional neural network （CNN）， long short-term memory network （LSTM）， and Attention mechanism. The model was trained and tested using historical measurement data. Finally， in simulation scenarios using the IEEE 33-node and PG&E69-node distribution systems， the superiority of this CNN-LSTM-Attention-based topology identification method over traditional approaches in terms of identification accuracy was validated， and online application of the model was achieved.https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=3d2aff3a-187d-49d6-8d55-eda22b006f15distribution networkstopology identificationconvolutional neural networklong short-term memory networkattention mechanism |
| spellingShingle | LING Jiakai ZHANG Yizhou HU Jinfeng QIN Jun DAI Jian FEI Youdie ZHU Zhen A real-time topology identification method of distribution networks based on CNN-LSTM-Attention Zhejiang dianli distribution networks topology identification convolutional neural network long short-term memory network attention mechanism |
| title | A real-time topology identification method of distribution networks based on CNN-LSTM-Attention |
| title_full | A real-time topology identification method of distribution networks based on CNN-LSTM-Attention |
| title_fullStr | A real-time topology identification method of distribution networks based on CNN-LSTM-Attention |
| title_full_unstemmed | A real-time topology identification method of distribution networks based on CNN-LSTM-Attention |
| title_short | A real-time topology identification method of distribution networks based on CNN-LSTM-Attention |
| title_sort | real time topology identification method of distribution networks based on cnn lstm attention |
| topic | distribution networks topology identification convolutional neural network long short-term memory network attention mechanism |
| url | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=3d2aff3a-187d-49d6-8d55-eda22b006f15 |
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