Fault Diagnosis Method for Hydropower Station Measurement and Control System Based on ISSA-VMD and 1DCNN-BiLSTM

Lin Wang, Fangqing Zhang (), Jiefei Wang, Gang Ren, Dengxian Wang, Ling Gao and Xingyu Ming
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Lin Wang: Xiluodu Hydropower Plant, China Yangtze Power Co., Ltd., Zhaotong 657300, China
Fangqing Zhang: School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Jiefei Wang: Xiluodu Hydropower Plant, China Yangtze Power Co., Ltd., Zhaotong 657300, China
Gang Ren: Xiluodu Hydropower Plant, China Yangtze Power Co., Ltd., Zhaotong 657300, China
Dengxian Wang: Xiluodu Hydropower Plant, China Yangtze Power Co., Ltd., Zhaotong 657300, China
Ling Gao: Xiluodu Hydropower Plant, China Yangtze Power Co., Ltd., Zhaotong 657300, China
Xingyu Ming: Xiluodu Hydropower Plant, China Yangtze Power Co., Ltd., Zhaotong 657300, China

Energies, 2024, vol. 17, issue 22, 1-25

Abstract: Sudden failures of measurement and control circuits in hydropower plants may lead to unplanned shutdowns of generating units. Therefore, the diagnosis of hydropower station measurement and control system poses a great challenge. Existing fault diagnosis methods suffer from long fault identification time, inaccurate positioning, and low diagnostic efficiency. In order to improve the accuracy of fault diagnosis, this paper proposes a fault diagnosis method for hydropower station measurement and control system that combines variational modal decomposition (VMD), Pearson’s correlation coefficient, a one-dimensional convolutional neural network, and a bi-directional long and short-term memory network (1DCNN-BiLSTM). Firstly, the VMD parameters are optimised by the Improved Sparrow Search Algorithm (ISSA). Secondly, signal decomposition of the original fault signals is carried out by using ISSA-VMD, and meanwhile, the optimal intrinsic modal components (IMFs) are screened out by using Pearson’s correlation coefficient, and the optimal set of components is subjected to signal reconstruction in order to obtain the new signal sequences. Then, the 1DCNN-BiLSTM-based fault diagnosis model is proposed, which achieves accurate diagnosis of the faults of hydropower station measurement and control system. Finally, experimental verification reveals that, in comparison with other methods such as 1DCNN, BiLSTM, ELM, BP neural network, SVM, and DBN, the proposed approach in this paper achieves an exceptionally high average recognition accuracy of 99.8% in both simulation and example analysis. Additionally, it demonstrates faster convergence speed, indicating not only its superior diagnostic precision but also its high application value.

Keywords: measurement and control system; fault diagnosis; improved sparrow search algorithm; variational modal decomposition; bidirectional long- and short-term memory networks (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: 2024
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