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METHODS article

Front. Energy Res.
Sec. Sustainable Energy Systems
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1468651
This article is part of the Research Topic Urban Energy System Planning, Operation, and Control with High Efficiency and Low Carbon Goals View all 29 articles

Matching method of power grid fault handling plan based on hybrid neural network

Provisionally accepted
Dajun Xiao Dajun Xiao 1Xialing Xu Xialing Xu 1Yue Zhang Yue Zhang 2Lianfei Shan Lianfei Shan 2Tao Liu Tao Liu 1Xin Li Xin Li 1Yongtian Qiao Yongtian Qiao 2Tao Jiang Tao Jiang 2Yu Wang Yu Wang 2*
  • 1 Central China Power Dispatching and Control Center of State Grid, WuHan, China
  • 2 Beijing KeDong Electric Power Control System Co., Ltd.,, Beijing, China

The final, formatted version of the article will be published soon.

    To improve the accuracy of online matching and pushing of power grid fault handling plan, a matching method of fault handling plan based on hybrid neural network is proposed. Firstly, 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 fault handling plan is trained by adjusting the hyper-parameters of the UIE framework. Then, the semantic distance between the fault equipment, fault type, fault phenomenon and the entity of 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, through the verification of fault related data of a regional power grid, the proposed fault handling plan matching method has higher matching accuracy and stronger generalization ability compared with 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 provide auxiliary decision for timely and rapid treatment of power grid faults.

    Keywords: power grid fault handling plan, universal information extraction framework, residual vector-embedding vector-encoded vector, Hybrid neural network model, Entity recognition, Text matching

    Received: 22 Jul 2024; Accepted: 13 Nov 2024.

    Copyright: © 2024 Xiao, Xu, Zhang, Shan, Liu, Li, Qiao, Jiang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yu Wang, Beijing KeDong Electric Power Control System Co., Ltd.,, Beijing, China

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