Novel abnormal data correction method based on GA-RBF neural network considering power balance

Xiwang Li; Shun Yan; Hai He; Li Lv; Ming Cai; Jia Cui; Chaoran Li

doi:10.1117/12.3024509

13 May 2024 Novel abnormal data correction method based on GA-RBF neural network considering power balance

Xiwang Li, Shun Yan, Hai He, Li Lv, Ming Cai, Jia Cui, Chaoran Li

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131599S (2024) https://doi.org/10.1117/12.3024509
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

In view of the shortcomings of the methods for correcting abnormal data in actual power grids, this paper proposes to use power balance method and GA-RBF neural network algorithm to correct abnormal data. Firstly, the power balance method is used to correct single abnormal data because of its simple principle and high accuracy. Secondly, the RBF neural network algorithm optimized by genetic algorithm is used to correct multiple interrelated abnormal data, which just makes up for the shortcomings of the previous correction method, and has the advantages of convenient operation and high calculation efficiency. Finally, the two methods are simulated and analyzed through a practical case.

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

Citation Download Citation

Xiwang Li, Shun Yan, Hai He, Li Lv, Ming Cai, Jia Cui, and Chaoran Li "Novel abnormal data correction method based on GA-RBF neural network considering power balance", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131599S (13 May 2024); https://doi.org/10.1117/12.3024509

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