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Acoustic Vibration Approach for Detecting Faults in Hydroelectric Units: A Review

Author

Listed:
  • Fang Dao

    (Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650031, China)

  • Yun Zeng

    (Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650031, China)

  • Yidong Zou

    (Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650031, China)

  • Xiang Li

    (Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650031, China)

  • Jing Qian

    (Faculty of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650031, China)

Abstract

The health of the hydroelectric generator determines the safe, stable, and reliable operation of the hydropower station. In order to keep the hydroelectric generator in a better state of health and avoid accidents, it is crucial to detect its faults. In recent years, fault detection methods based on sound and vibration signals have gradually become research hotspots due to their high sensitivity, achievable continuous dynamic monitoring, and easy adaptation to complex environments. Therefore, this paper is a supplement to the existing state monitoring and fault diagnosis system of the hydroelectric generator; it divides the hydroelectric generator into two significant parts: hydro-generator and hydro-turbine, and summarizes the research and application of fault detect technology based on sound signal vibration in hydroelectric generator and introduces some new technology developments in recent years, and puts forward the existing problems in the current research and future development directions, and it is expected to provides some reference for the research on fault diagnosis of the hydroelectric generator.

Suggested Citation

  • Fang Dao & Yun Zeng & Yidong Zou & Xiang Li & Jing Qian, 2021. "Acoustic Vibration Approach for Detecting Faults in Hydroelectric Units: A Review," Energies, MDPI, vol. 14(23), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7840-:d:685536
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    References listed on IDEAS

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    Cited by:

    1. Dao, Fang & Zou, Yidong & Zeng, Yun & Qian, Jing & Li, Xiang, 2023. "An intelligent CPSOGSA-based mixed H2/H∞ robust controller for the multi-hydro-turbine governing system with sharing common penstock," Renewable Energy, Elsevier, vol. 206(C), pages 481-497.
    2. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).

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