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A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines

Author

Listed:
  • Qian Li

    (Huaneng Clean Energy Research Institute, Huaneng Group Ltd., Beijing 102209, China
    These authors contributed equally to this work.)

  • Danyang Chen

    (The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
    These authors contributed equally to this work.)

  • Hangbing Lin

    (Huaneng Clean Energy Research Institute, Huaneng Group Ltd., Beijing 102209, China)

  • Xiaolei Yang

    (The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
    School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Yaw errors occur in wind turbines either during the installation stage or because of the aging of devices. It reduces the wind speed in the rotor axial direction and increases the structural loads in the lateral direction. Diagnosing yaw error rapidly and accurately is crucial for avoiding the introduced under-performance. In this review paper, we first introduce the fundamental concepts and principles of wind turbine yaw control strategies, and we discuss two types of yaw errors (i.e., the static yaw error and the dynamic yaw error) with their corresponding causes. Subsequently, we outline the existing yaw error diagnostic methods, which are based on the LiDAR (light detection and ranging) data, the SCADA (supervisory control and data acquisition) data, or a combination of the two, and we discuss the advantages and disadvantages of various methods. At last, we emphasize that the diagnostic performance can be improved via the combination of the LiDAR data and the SCADA data, and it benefits from an in-depth understanding of the salient features and influential factors associated with the yaw error. Meanwhile, the potential of intelligent clusters and digital twins for detecting yaw errors is discussed.

Suggested Citation

  • Qian Li & Danyang Chen & Hangbing Lin & Xiaolei Yang, 2025. "A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines," Energies, MDPI, vol. 18(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:3:p:588-:d:1577755
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    References listed on IDEAS

    as
    1. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Predictive model of yaw error in a wind turbine," Energy, Elsevier, vol. 123(C), pages 119-130.
    2. Lopez, Daniel & Kuo, Jim & Li, Ni, 2019. "A novel wake model for yawed wind turbines," Energy, Elsevier, vol. 178(C), pages 158-167.
    3. Rauh, A. & Seelert, W., 1984. "The Betz optimum efficiency for windmills," Applied Energy, Elsevier, vol. 17(1), pages 15-23.
    4. Jing, Bo & Qian, Zheng & Pei, Yan & Zhang, Lizhong & Yang, Tingyi, 2020. "Improving wind turbine efficiency through detection and calibration of yaw misalignment," Renewable Energy, Elsevier, vol. 160(C), pages 1217-1227.
    5. Jeong, Min-Soo & Kim, Sang-Woo & Lee, In & Yoo, Seung-Jae & Park, K.C., 2013. "The impact of yaw error on aeroelastic characteristics of a horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 60(C), pages 256-268.
    6. Shuting Wan & Lifeng Cheng & Xiaoling Sheng, 2015. "Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model," Energies, MDPI, vol. 8(7), pages 1-16, June.
    7. Davide Astolfi & Ravi Pandit & Linyue Gao & Jiarong Hong, 2022. "Individuation of Wind Turbine Systematic Yaw Error through SCADA Data," Energies, MDPI, vol. 15(21), pages 1-5, November.
    Full references (including those not matched with items on IDEAS)

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