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Research on anti-error operation warning of power grid dispatching based on deep bidirectional gated recurrent neural network

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  • @ARTICLE{10.4108/ew.9071,
        author={Jinglong He and Dunlin Zhu and Sheng Yang and Jinming Liu and Tianyun Luo and Yuan Fu},
        title={Research on anti-error operation warning of power grid dispatching based on deep bidirectional gated recurrent neural network},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={12},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2025},
        month={4},
        keywords={Deep learning, Bidirectional gating unit, Recurrent neural network, Grid dispatch error prevention, Early warning},
        doi={10.4108/ew.9071}
    }
    
  • Jinglong He
    Dunlin Zhu
    Sheng Yang
    Jinming Liu
    Tianyun Luo
    Yuan Fu
    Year: 2025
    Research on anti-error operation warning of power grid dispatching based on deep bidirectional gated recurrent neural network
    EW
    EAI
    DOI: 10.4108/ew.9071
Jinglong He1, Dunlin Zhu1, Sheng Yang1,*, Jinming Liu1, Tianyun Luo1, Yuan Fu1
  • 1: Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China
*Contact email: shetang67347700425@163.com

Abstract

To improve the security and overall efficiency of grid scheduling work and accurately optimize scheduling decisions, a grid scheduling error-proof operation warning method based on a deep bidirectional gated recurrent neural network is proposed. This paper combines the principle of hierarchical data construction, summarizes the structured data of metadata operation tickets and maintenance plans of CIM model and OMS network frame model, and constructs the data warehouse of grid dispatching error prevention; based on the natural language processing (NLP) technology, key information and knowledge entities related to grid dispatching error prevention are automatically identified and extracted from the data warehouse. Based on the deep bidirectional gated recurrent neural network, the extracted information sequence is used as input to construct the grid scheduling operation state reconstruction model, and the error prevention warning is carried out according to the output prediction results. The experimental results show that: the data docking speed in different scheduling phases is fast with the fastest speed of 71.254MB/s, and the convergence speed of the analysis and calculation is within 0.01MB/s, indicating that the overall analysis efficiency is high, the application performance is good, and it can determine whether there is any misoperation in the process of grid scheduling and carry out highly efficient, accurate, and fast early warning.

Keywords
Deep learning, Bidirectional gating unit, Recurrent neural network, Grid dispatch error prevention, Early warning
Received
2024-08-20
Accepted
2024-09-20
Published
2025-04-11
Publisher
EAI
http://dx.doi.org/10.4108/ew.9071

Copyright © 2025 Jinglong He et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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