Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data

Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang () and Ting Liu
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Bei Liu: SDIC Gansu Xiaosanxia Power Co., Ltd., Lanzhou 730050, China
Xiao Wang: SDIC Gansu Xiaosanxia Power Co., Ltd., Lanzhou 730050, China
Zhaoxin Zhang: SDIC Gansu Xiaosanxia Power Co., Ltd., Lanzhou 730050, China
Zhenjie Zhao: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Xiaoming Wang: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Ting Liu: School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

Energies, 2025, vol. 18, issue 14, 1-21

Abstract: Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by 5.5 % , 4.8 % , 7.8 % , and 9.3 % , with at least a 160 % improvement in the fault recall rate.

Keywords: failure prediction; hydropower station; biased data; CNN-LSTM; multi-scale feature extraction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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