Fault prediction method of hydro turbine generator set based on CFA-Conv-LSTM

Jian Zhang; Yongxin Sun; Guangdong Yang; Hongyu Cao; Jie Zeng; Bin Liang

doi:10.1117/12.3050303

12 December 2024 Fault prediction method of hydro turbine generator set based on CFA-Conv-LSTM

Jian Zhang, Yongxin Sun, Guangdong Yang, Hongyu Cao, Jie Zeng, Bin Liang

Author Affiliations +

Proceedings Volume 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024); 1341931 (2024) https://doi.org/10.1117/12.3050303
Event: Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 2024, Lhasa, China

Abstract

The hydraulic, mechanical and electrical factors are coupled with each other during the operation of the hydroelectric generating set, which is highly nonlinear, unstable and time-varying, and it is difficult to accurately model and predict. Aiming at the problem of hydraulic turbine generator failure prediction, this paper attempts to introduce convolution (Conv), integrate long short-term memory neural network (Long Short Term Memory, LSTM), and propose a Conv-LSTM deep learning prediction network. At the same time, in order to solve the problem of hyperparameter optimization, the chaotic firefly algorithm (CFA) was introduced, and a fault prediction method for hydro turbine generator sets based on CFA-Conv-LSTM was constructed. Using the measured data of No. 1 unit of a hydropower station in Guangxi, compared with other commonly used methods, it is proved that the method is significantly better than other methods in terms of fault prediction accuracy and process monitoring performance, and has good application prospects and promotion value.

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

Citation Download Citation

Jian Zhang, Yongxin Sun, Guangdong Yang, Hongyu Cao, Jie Zeng, and Bin Liang "Fault prediction method of hydro turbine generator set based on CFA-Conv-LSTM", Proc. SPIE 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 1341931 (12 December 2024); https://doi.org/10.1117/12.3050303

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