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Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function

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

Abstract

Wind turbine blades are critical components in wind energy generation, and blade health management is a challenging issue for the operation and maintenance of wind turbines. In this paper, an adaptive method is developed to identify blade damages based on the microphone array and compressive beamforming, and global and remote health assessment can be accomplished. In this method, the generalized minimax-concave penalty function is employed to enhance sparse recovery capacities, and step-sizes in computation processes are adjusted adaptively to adapt to variational conditions. Besides, potential damage locations are extracted in coarse acoustic maps to improve convergence rates. Numerical simulations show that high spatial resolutions can be achieved by the proposed method, and the computation time for solving acoustic inverse problems is less than using existing algorithms, especially with low-frequency sources. Moreover, experiments are conducted with a small-scale wind turbine. Results demonstrate that several damages in operating blades can be precisely recognized with high efficiencies, and the deterioration of acoustic maps induced by improper step-sizes can be avoided. The proposed method provides a promising way for in-situ health monitoring of wind turbine blades.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:59-70
    DOI: 10.1016/j.renene.2021.09.024
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    References listed on IDEAS

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    1. NESTEROV, Yurii, 2013. "Gradient methods for minimizing composite functions," LIDAM Reprints CORE 2510, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Lux, Philipp & Cassano, Alessandro G. & Johnson, Stephen B. & Maiaru, Marianna & Stapleton, Scott E., 2020. "Adhesive curing cycle time optimization in wind turbine blade manufacturing," Renewable Energy, Elsevier, vol. 162(C), pages 397-410.
    3. Lee, Jae-Kyung & Park, Joon-Young & Oh, Ki-Yong & Ju, Seung-Hwan & Lee, Jun-Shin, 2015. "Transformation algorithm of wind turbine blade moment signals for blade condition monitoring," Renewable Energy, Elsevier, vol. 79(C), pages 209-218.
    4. Mishnaevsky, Leon, 2019. "Repair of wind turbine blades: Review of methods and related computational mechanics problems," Renewable Energy, Elsevier, vol. 140(C), pages 828-839.
    5. Laurent Condat, 2013. "A Primal–Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms," Journal of Optimization Theory and Applications, Springer, vol. 158(2), pages 460-479, August.
    6. Patrick L. Combettes & Jean-Christophe Pesquet, 2011. "Proximal Splitting Methods in Signal Processing," Springer Optimization and Its Applications, in: Heinz H. Bauschke & Regina S. Burachik & Patrick L. Combettes & Veit Elser & D. Russell Luke & Henry (ed.), Fixed-Point Algorithms for Inverse Problems in Science and Engineering, chapter 0, pages 185-212, Springer.
    7. Yang, Ruizhen & He, Yunze & Zhang, Hong, 2016. "Progress and trends in nondestructive testing and evaluation for wind turbine composite blade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1225-1250.
    8. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.
    9. Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
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    Cited by:

    1. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
    2. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    3. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    4. 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).
    5. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy," Applied Energy, Elsevier, vol. 313(C).
    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. 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).
    8. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. 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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