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

Author

Listed:
  • 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
    Wuhan Relabo Technology Co., Ltd., 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)

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.

Suggested Citation

  • Lin Wang & Fangqing Zhang & Jiefei Wang & Gang Ren & Dengxian Wang & Ling Gao & Xingyu Ming, 2024. "Fault Diagnosis Method for Hydropower Station Measurement and Control System Based on ISSA-VMD and 1DCNN-BiLSTM," Energies, MDPI, vol. 17(22), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5686-:d:1520555
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    References listed on IDEAS

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    1. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    2. Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
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