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Research on Wind Turbine Fault Detection Based on CNN-LSTM

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  • Lin Qi

    (School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
    Beijing Key Lab of Green Development Decision Based on Big Data, Beijing 102206, China)

  • Qianqian Zhang

    (School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
    Beijing World Urban Circular Economy System (Industry) Collaborative Innovation Center, Beijing 100192, China)

  • Yunjie Xie

    (School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
    Beijing World Urban Circular Economy System (Industry) Collaborative Innovation Center, Beijing 100192, China)

  • Jian Zhang

    (School of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China
    Beijing World Urban Circular Economy System (Industry) Collaborative Innovation Center, Beijing 100192, China)

  • Jinran Ke

    (Beijing World Urban Circular Economy System (Industry) Collaborative Innovation Center, Beijing 100192, China)

Abstract

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Suggested Citation

  • Lin Qi & Qianqian Zhang & Yunjie Xie & Jian Zhang & Jinran Ke, 2024. "Research on Wind Turbine Fault Detection Based on CNN-LSTM," Energies, MDPI, vol. 17(17), pages 1-21, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4497-:d:1473525
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    References listed on IDEAS

    as
    1. Kusiak, Andrew & Verma, Anoop, 2012. "Analyzing bearing faults in wind turbines: A data-mining approach," Renewable Energy, Elsevier, vol. 48(C), pages 110-116.
    2. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
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