Fast reconfiguration method of low-carbon distribution network based on convolutional neural network
The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon e...
| 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.2023.1102949/full |
| _version_ | 1797948109851983872 |
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| author | Yixiao Yu Ming Yang Yumin Zhang Pingfeng Ye Xingquan Ji Jingrui Li |
| author_facet | Yixiao Yu Ming Yang Yumin Zhang Pingfeng Ye Xingquan Ji Jingrui Li |
| author_sort | Yixiao Yu |
| collection | DOAJ |
| description | The existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand. |
| first_indexed | 2024-04-10T21:38:16Z |
| format | Article |
| id | doaj.art-918a5903cc0b42c1be56e4e0a20ca0fe |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T21:38:16Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-918a5903cc0b42c1be56e4e0a20ca0fe2023-01-19T08:17:16ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011110.3389/fenrg.2023.11029491102949Fast reconfiguration method of low-carbon distribution network based on convolutional neural networkYixiao Yu0Ming Yang1Yumin Zhang2Pingfeng Ye3Xingquan Ji4Jingrui Li5Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan, Shandong Province, ChinaKey Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan, Shandong Province, ChinaKey Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan, Shandong Province, ChinaCollege of Energy Storage Technology, Shandong University of Science and Technology, Qingdao, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, ChinaThe existing meta-heuristic distribution network reconfiguration (DNR) algorithm has excellent optimization ability through iteration. However, it is difficult to realize the large-scale fast calculation and online real-time response of DNR solution. In order to improve the security and low-carbon economy of distribution network, this paper proposes a fast reconfiguration method of distribution network based on convolutional neural network (CNN). Taking IEEE 33 system and 185 node system as examples, the effectiveness of the proposed method is verified. The reasons why the proposed method can achieve better results are as follows: By mining the historical data of distribution network, the corresponding relationship between load mode (LM) and its optimal topology is established. For a load mode in actual operation, the reconfiguration scheme can be quickly obtained according to the established corresponding relationship. Thus, iterative calculation is avoided and computational efficiency is improved. A multi-branch CNN model is established based on the distribution network structure, and an inception module is introduced into CNN to improve the ability of CNN to extract data features. This model can reduce the dependence on the specific distribution network structure and is easy to expand.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1102949/fullconvolutional neural network (CNN)rapid reconfiguration of distribution networkload modemixed trainingdata driven |
| spellingShingle | Yixiao Yu Ming Yang Yumin Zhang Pingfeng Ye Xingquan Ji Jingrui Li Fast reconfiguration method of low-carbon distribution network based on convolutional neural network Frontiers in Energy Research convolutional neural network (CNN) rapid reconfiguration of distribution network load mode mixed training data driven |
| title | Fast reconfiguration method of low-carbon distribution network based on convolutional neural network |
| title_full | Fast reconfiguration method of low-carbon distribution network based on convolutional neural network |
| title_fullStr | Fast reconfiguration method of low-carbon distribution network based on convolutional neural network |
| title_full_unstemmed | Fast reconfiguration method of low-carbon distribution network based on convolutional neural network |
| title_short | Fast reconfiguration method of low-carbon distribution network based on convolutional neural network |
| title_sort | fast reconfiguration method of low carbon distribution network based on convolutional neural network |
| topic | convolutional neural network (CNN) rapid reconfiguration of distribution network load mode mixed training data driven |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1102949/full |
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