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In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures

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  • Sun, Shilin
  • Wang, Tianyang
  • Chu, Fulei

Abstract

Blades are critical components in wind turbines (WTs) for power generation, and condition monitoring (CM) of WT blades is a crucial and challenging issue under operating conditions. Several methods have been developed to assess blade health status, and some of them have the potential to achieve in-situ CM. Nevertheless, there is a lack of critical and comprehensive survey concerning CM techniques for operational WT blades, as well as unsolved problems and research prospects. In this paper, typical causes and types of blade damage that deserve to be considered during the running time are presented. Besides, in-situ CM techniques for WT blades are reviewed in terms of four sub-targets: damage detection, localization, classification, and evaluation. Further, the investigation and development timeline of in-situ CM methods and types of damage that have been verified to be recognizable in field tests are concluded for the first time. The paper ends with discussions on research impediments and prospects that indicate promising development tendencies.

Suggested Citation

  • Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:rensus:v:160:y:2022:i:c:s1364032122002404
    DOI: 10.1016/j.rser.2022.112326
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    Cited by:

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    2. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    3. Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Sun, Shilin & Li, Qi & Hu, Wenyang & Liang, Zhongchao & Wang, Tianyang & Chu, Fulei, 2023. "Wind turbine blade breakage detection based on environment-adapted contrastive learning," Renewable Energy, Elsevier, vol. 219(P2).
    5. Liu, Dongdong & Cui, Lingli & Cheng, Weidong, 2023. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation," Renewable Energy, Elsevier, vol. 206(C), pages 645-657.
    6. Tang, Yaochi & Chang, Yunchi & Li, Kuohao, 2023. "Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage," Renewable Energy, Elsevier, vol. 212(C), pages 855-864.
    7. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.

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