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Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection

Author

Listed:
  • Chang Cai

    (Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China)

  • Jicai Guo

    (College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot 010051, China)

  • Xiaowen Song

    (College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot 010051, China)

  • Yanfeng Zhang

    (College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot 010051, China)

  • Jianxin Wu

    (College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot 010051, China)

  • Shufeng Tang

    (College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot 010051, China)

  • Yan Jia

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China)

  • Zhitai Xing

    (College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China)

  • Qing’an Li

    (Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

Onshore wind turbines are primarily installed in high-altitude areas with good wind energy resources. However, in winter, the blades are easy to ice, which will seriously impact their aerodynamic performance, as well as the power and service life of the wind turbine. Therefore, it is of great practical significance to predict wind turbine blade icing in advance and take measures to eliminate the adverse effects of icing. Along these lines, three approaches to supervisory control and data acquisition (SCADA) data feature selection were summarized in this work. The problems of imbalance between positive and negative sample datasets, the underutilization of SCADA data time series information, the scarcity of high-quality labeled data, and weak model generalization capabilities faced by data-driven approaches in wind turbine blade icing detection, were reviewed. Finally, some future trends in data-driven approaches were discussed. Our work provides guidance for the use of technical means in the actual detection of wind turbine blades. In addition, it also gives some insights to the further research of fault diagnosis technology.

Suggested Citation

  • Chang Cai & Jicai Guo & Xiaowen Song & Yanfeng Zhang & Jianxin Wu & Shufeng Tang & Yan Jia & Zhitai Xing & Qing’an Li, 2023. "Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1617-:d:1035458
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    References listed on IDEAS

    as
    1. Hacıefendioğlu, Kemal & Başağa, Hasan Basri & Yavuz, Zafer & Karimi, Mohammad Tordi, 2022. "Intelligent ice detection on wind turbine blades using semantic segmentation and class activation map approaches based on deep learning method," Renewable Energy, Elsevier, vol. 182(C), pages 1-16.
    2. 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.
    3. Tao, Tao & Liu, Yongqian & Qiao, Yanhui & Gao, Linyue & Lu, Jiaoyang & Zhang, Ce & Wang, Yu, 2021. "Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 1004-1013.
    4. Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
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