A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
This paper proposes a data-driven state estimation based on sample migration for low-observable distribution networks, addressing the challenge of traditional state estimators being unsuitable for distribution networks with low observability. The state estimation model is trained using historical me...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/3/121 |
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| author | Hao Jiao Chen Wu Lei Wei Jinming Chen Yang Xu Manyun Huang |
| author_facet | Hao Jiao Chen Wu Lei Wei Jinming Chen Yang Xu Manyun Huang |
| author_sort | Hao Jiao |
| collection | DOAJ |
| description | This paper proposes a data-driven state estimation based on sample migration for low-observable distribution networks, addressing the challenge of traditional state estimators being unsuitable for distribution networks with low observability. The state estimation model is trained using historical measurement data from distribution networks with high observability. Measurements updated for low-observable distribution networks are supplemented by transferring samples from high-observable distribution networks using sample migration techniques, resulting in a state estimation model suitable for low-observable distribution networks. Test results demonstrate that the proposed algorithm outperforms traditional algorithms in both estimation accuracy and robustness aspects, such as the Weighted Least Squares (WLS) and Weighted Least Absolute Value (WLAV) methods. Furthermore, sample migration enhances the generalization ability of the state estimation model. |
| format | Article |
| id | doaj-art-1a2e21eef2ca429cbeaaab43d55b7fca |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-1a2e21eef2ca429cbeaaab43d55b7fca2025-08-20T02:11:15ZengMDPI AGAlgorithms1999-48932025-02-0118312110.3390/a18030121A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution NetworksHao Jiao0Chen Wu1Lei Wei2Jinming Chen3Yang Xu4Manyun Huang5State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaState Grid Jiangsu Electric Power Science Research Institute, Nanjing 211103, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing 211100, ChinaThis paper proposes a data-driven state estimation based on sample migration for low-observable distribution networks, addressing the challenge of traditional state estimators being unsuitable for distribution networks with low observability. The state estimation model is trained using historical measurement data from distribution networks with high observability. Measurements updated for low-observable distribution networks are supplemented by transferring samples from high-observable distribution networks using sample migration techniques, resulting in a state estimation model suitable for low-observable distribution networks. Test results demonstrate that the proposed algorithm outperforms traditional algorithms in both estimation accuracy and robustness aspects, such as the Weighted Least Squares (WLS) and Weighted Least Absolute Value (WLAV) methods. Furthermore, sample migration enhances the generalization ability of the state estimation model.https://www.mdpi.com/1999-4893/18/3/121low-observable distribution networksartificial intelligencestate estimationsample migration |
| spellingShingle | Hao Jiao Chen Wu Lei Wei Jinming Chen Yang Xu Manyun Huang A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks Algorithms low-observable distribution networks artificial intelligence state estimation sample migration |
| title | A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks |
| title_full | A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks |
| title_fullStr | A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks |
| title_full_unstemmed | A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks |
| title_short | A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks |
| title_sort | data driven state estimation based on sample migration for low observable distribution networks |
| topic | low-observable distribution networks artificial intelligence state estimation sample migration |
| url | https://www.mdpi.com/1999-4893/18/3/121 |
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