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An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach

Author

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  • Jianjun Chen

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Weihao Hu

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Di Cao

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Bin Zhang

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Qi Huang

    (School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Zhe Chen

    (Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark)

  • Frede Blaabjerg

    (Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark)

Abstract

Wind power penetration has increased rapidly in recent years. In winter, the wind turbine blade imbalance fault caused by ice accretion increase the maintenance costs of wind farms. It is necessary to detect the fault before blade breakage occurs. Preliminary analysis of time series simulation data shows that it is difficult to detect the imbalance faults by traditional mathematical methods, as there is little difference between normal and fault conditions. A deep learning method for wind turbine blade imbalance fault detection and classification is proposed in this paper. A long short-term memory (LSTM) neural network model is built to extract the characteristics of the fault signal. The attention mechanism is built into the LSTM to increase its performance. The simulation results show that the proposed approach can detect the imbalance fault with an accuracy of over 98%, which proves the effectiveness of the proposed approach on wind turbine blade imbalance fault detection.

Suggested Citation

  • Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2764-:d:249639
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    References listed on IDEAS

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    Cited by:

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    2. Hongwei Li & Kaide Ren & Shuaibing Li & Haiying Dong, 2020. "Adaptive Multi-Model Switching Predictive Active Power Control Scheme for Wind Generator System," Energies, MDPI, vol. 13(6), pages 1-12, March.
    3. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.

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