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Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage

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  • Tang, Yaochi
  • Chang, Yunchi
  • Li, Kuohao

Abstract

Heavy losses in the wind power generation system are incurred when the wind turbine blades are severely damaged beyond repair. Therefore, an immediate repair at an initial stage is an economical method. There are limitations and difficulties in diagnosing initial damage to wind turbine blades. For example, the measurement cannot be performed during actual operation, the diagnostic machine learning model calculations are too complex, and so on. This study measured the noise signals in the operation of wind turbines. The k-nearest neighbor (k-NN) of supervised learning was used as the diagnostic method. The calculation of generalized fractal dimensions (GFDs) was used as diagnostic feature selection. The result shows an accuracy of 98.9%. Compared to other algorithms, the k-NN algorithm is simple and easy to understand and implement. To reduce the amount of calculation of the machine learning model, the optimum numerical combination of three major parameters is found in this study. These major parameters are (1) scale index of GFDs, (2) number of neighbor points in the algorithm, and (3) range formula. According to the findings, the k-NN algorithm model can achieve high accuracy. The optimum numerical combination of three major parameters provides a rapid, convenient, and efficient diagnostic method.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:212:y:2023:i:c:p:855-864
    DOI: 10.1016/j.renene.2023.05.087
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    References listed on IDEAS

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    1. Chandrasekhar, Kartik & Stevanovic, Nevena & Cross, Elizabeth J. & Dervilis, Nikolaos & Worden, Keith, 2021. "Damage detection in operational wind turbine blades using a new approach based on machine learning," Renewable Energy, Elsevier, vol. 168(C), pages 1249-1264.
    2. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    3. Chen, Jinglong & Pan, Jun & Zhang, Chunlin & Luo, Xiaoyu & Zhou, Zitong & Wang, Biao, 2017. "Specialization improved nonlocal means to detect periodic impulse feature for generator bearing fault identification," Renewable Energy, Elsevier, vol. 103(C), pages 448-467.
    4. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
    5. 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).
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