Power grid fault handling plan matching method based on a hybrid neural network
A matching method based on a hybrid neural network is proposed to improve the accuracy of online matching for a power grid fault handling plan. First, the ERNIE 3.0 encoding and double-pointer decoding module are used to replace the generative model in the universal information extraction (UIE) fram...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1468651/full |
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| author | Dajun Xiao Xialing Xu Bo Zhang Yue Zhang Lianfei Shan Tao Liu Xin Li Yongtian Qiao Tao Jiang Yu Wang |
| author_facet | Dajun Xiao Xialing Xu Bo Zhang Yue Zhang Lianfei Shan Tao Liu Xin Li Yongtian Qiao Tao Jiang Yu Wang |
| author_sort | Dajun Xiao |
| collection | DOAJ |
| description | A matching method based on a hybrid neural network is proposed to improve the accuracy of online matching for a power grid fault handling plan. First, the ERNIE 3.0 encoding and double-pointer decoding module are used to replace the generative model in the universal information extraction (UIE) framework, and the mapping relationship between entities and entity labels of the fault handling plan is trained by adjusting the hyperparameters of the UIE framework. Then, the semantic distance between the fault equipment, fault type, fault phenomenon, and the entity of the fault handling plan is calculated based on the residual vector-embedding vector-encoded vector (RE2). The hybrid neural network model for power grid fault handling plan matching is established. Finally, by verifying the fault-related data of a regional power grid, the proposed fault handling plan matching method shows higher matching accuracy and stronger generalization ability than other algorithms. The average precision rate, recall rate, and F1 value of the built fault handling plan matching model are 97.61%, 98.24%, and 97.91%, respectively, which can support auxiliary decisions for timely and rapid treatment of power grid faults. |
| format | Article |
| id | doaj-art-c2b437dade4a4ea99ad6ff0ff235d904 |
| institution | OA Journals |
| issn | 2296-598X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-c2b437dade4a4ea99ad6ff0ff235d9042025-08-20T02:32:29ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-12-011210.3389/fenrg.2024.14686511468651Power grid fault handling plan matching method based on a hybrid neural networkDajun Xiao0Xialing Xu1Bo Zhang2Yue Zhang3Lianfei Shan4Tao Liu5Xin Li6Yongtian Qiao7Tao Jiang8Yu Wang9Central China branch of State Grid Co., Ltd. Central China Power Dispatching and Control Center of State Grid, Beijing, ChinaCentral China branch of State Grid Co., Ltd. Central China Power Dispatching and Control Center of State Grid, Beijing, ChinaBeijing Kedong Electric Power Control System Co., Ltd., Beijing, ChinaBeijing Kedong Electric Power Control System Co., Ltd., Beijing, ChinaBeijing Kedong Electric Power Control System Co., Ltd., Beijing, ChinaCentral China branch of State Grid Co., Ltd. Central China Power Dispatching and Control Center of State Grid, Beijing, ChinaCentral China branch of State Grid Co., Ltd. Central China Power Dispatching and Control Center of State Grid, Beijing, ChinaBeijing Kedong Electric Power Control System Co., Ltd., Beijing, ChinaBeijing Kedong Electric Power Control System Co., Ltd., Beijing, ChinaBeijing Kedong Electric Power Control System Co., Ltd., Beijing, ChinaA matching method based on a hybrid neural network is proposed to improve the accuracy of online matching for a power grid fault handling plan. First, the ERNIE 3.0 encoding and double-pointer decoding module are used to replace the generative model in the universal information extraction (UIE) framework, and the mapping relationship between entities and entity labels of the fault handling plan is trained by adjusting the hyperparameters of the UIE framework. Then, the semantic distance between the fault equipment, fault type, fault phenomenon, and the entity of the fault handling plan is calculated based on the residual vector-embedding vector-encoded vector (RE2). The hybrid neural network model for power grid fault handling plan matching is established. Finally, by verifying the fault-related data of a regional power grid, the proposed fault handling plan matching method shows higher matching accuracy and stronger generalization ability than other algorithms. The average precision rate, recall rate, and F1 value of the built fault handling plan matching model are 97.61%, 98.24%, and 97.91%, respectively, which can support auxiliary decisions for timely and rapid treatment of power grid faults.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1468651/fullpower grid fault handling planuniversal information extraction frameworkresidual vector-embedding vector-encoded vectorhybrid neural network modelentity recognitiontext matching |
| spellingShingle | Dajun Xiao Xialing Xu Bo Zhang Yue Zhang Lianfei Shan Tao Liu Xin Li Yongtian Qiao Tao Jiang Yu Wang Power grid fault handling plan matching method based on a hybrid neural network Frontiers in Energy Research power grid fault handling plan universal information extraction framework residual vector-embedding vector-encoded vector hybrid neural network model entity recognition text matching |
| title | Power grid fault handling plan matching method based on a hybrid neural network |
| title_full | Power grid fault handling plan matching method based on a hybrid neural network |
| title_fullStr | Power grid fault handling plan matching method based on a hybrid neural network |
| title_full_unstemmed | Power grid fault handling plan matching method based on a hybrid neural network |
| title_short | Power grid fault handling plan matching method based on a hybrid neural network |
| title_sort | power grid fault handling plan matching method based on a hybrid neural network |
| topic | power grid fault handling plan universal information extraction framework residual vector-embedding vector-encoded vector hybrid neural network model entity recognition text matching |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1468651/full |
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