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

Jinglong He; Dunlin Zhu; Sheng Yang; Jinming Liu; Tianyun Luo; Yuan Fu

doi:10.4108/ew.9071

Authors

Jinglong He Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China
Dunlin Zhu Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China
Sheng Yang Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China
Jinming Liu Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China
Tianyun Luo Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China
Yuan Fu Power Dispatching Control Center, Guangxi Power Grid, Nanning, Guangxi, 530000, China

DOI:

https://doi.org/10.4108/ew.9071

Keywords:

Deep learning, Bidirectional gating unit, Recurrent neural network, Grid dispatch error prevention, Early warning

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.

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