Substation flood control risk prediction based on improved neural network

Kailing Chen; Xinqian Xia; Zhaojing Li

doi:10.1117/12.3024577

13 May 2024 Substation flood control risk prediction based on improved neural network

Kailing Chen, Xinqian Xia, Zhaojing Li

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131591M (2024) https://doi.org/10.1117/12.3024577
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

With the frequent extreme rainstorm and flood events at present, it is necessary to carry out flood control transformation of substation in operation to avoid its damage by flood. By identifying and evaluating risks and taking corresponding pre-control measures, the risk of substation suffering from flooding can be reduced to the greatest extent. This paper presents a method for predicting substation flood control risk. Firstly, the finite element method (FEM) is used to preprocess the historical data of substation flood control. Secondly, the improved neural network model is constructed, and part of the data is used to complete the model training. Finally, the actual substation data verification shows that the accuracy of the model is higher than that of the traditional neural network model, which is a simple and effective method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kailing Chen, Xinqian Xia, and Zhaojing Li "Substation flood control risk prediction based on improved neural network", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131591M (13 May 2024); https://doi.org/10.1117/12.3024577

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