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Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades

Author

Listed:
  • Wenjie Wang

    (College of Engineering, Ocean University of China, Qingdao 266100, China)

  • Yu Xue

    (College of Engineering, Ocean University of China, Qingdao 266100, China)

  • Chengkuan He

    (College of Engineering, Ocean University of China, Qingdao 266100, China)

  • Yongnian Zhao

    (College of Engineering, Ocean University of China, Qingdao 266100, China)

Abstract

With global warming and the depletion of fossil energy sources, renewable energy is gradually replacing non-renewable energy as the main energy in the future. As one of the fastest growing renewable energy sources, the safety and reliability of wind energy have been paid more and more attention. The size of modern wind turbines is becoming larger and larger. As the main component of wind turbines to capture energy, the blade is often damaged by various complex environments and irregular loads. Therefore, the health monitoring and damage identification of wind turbine blades have become a main research focus. At present, in addition to the overview of various detection methods of wind turbine blades, there is a lack of comprehensive classifications and overviews of the main damage types, damage-generation mechanisms, and basic principles of the damage-detection technology of wind turbine blades. In this paper, firstly, the common fault types of wind turbine blades, such as trailing edge cracking, lightning strike, leading edge corrosion pollution, icing, and delamination, as well as their generation mechanism, are comprehensively analyzed. Then, the basic principles and the latest research progress of the current main detection technologies, such as vision, ultrasonic, thermal imaging, vibration, acoustic emission, and so on, are comprehensively reviewed. The advantages and limitations of the various detection technologies for practical application are summarized. Finally, through a comparative analysis of the various damage-detection technologies, we try to find potential future research directions, and draw conclusions. This paper will provide a reference for understanding the mechanism behind the main damage types and the damage-detection methods of wind turbine blades. It has important reference value for further promoting practical research of wind turbine blade damage-detection technology and grasping this research direction.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5672-:d:880448
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    References listed on IDEAS

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    1. Dollinger, Christoph & Balaresque, Nicholas & Gaudern, Nicholas & Gleichauf, Daniel & Sorg, Michael & Fischer, Andreas, 2019. "IR thermographic flow visualization for the quantification of boundary layer flow disturbances due to the leading edge condition," Renewable Energy, Elsevier, vol. 138(C), pages 709-721.
    2. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    3. Mishnaevsky, Leon & Hasager, Charlotte Bay & Bak, Christian & Tilg, Anna-Maria & Bech, Jakob I. & Doagou Rad, Saeed & Fæster, Søren, 2021. "Leading edge erosion of wind turbine blades: Understanding, prevention and protection," Renewable Energy, Elsevier, vol. 169(C), pages 953-969.
    4. Haselbach, P.U. & Bitsche, R.D. & Branner, K., 2016. "The effect of delaminations on local buckling in wind turbine blades," Renewable Energy, Elsevier, vol. 85(C), pages 295-305.
    5. 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.
    6. Tang, Jialin & Soua, Slim & Mares, Cristinel & Gan, Tat-Hean, 2016. "An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades," Renewable Energy, Elsevier, vol. 99(C), pages 170-179.
    7. Ozbek, Muammer & Rixen, Daniel J. & Erne, Oliver & Sanow, Gunter, 2010. "Feasibility of monitoring large wind turbines using photogrammetry," Energy, Elsevier, vol. 35(12), pages 4802-4811.
    8. Han, Woobeom & Kim, Jonghwa & Kim, Bumsuk, 2018. "Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 817-823.
    9. Mufutau Opeyemi, Bello, 2021. "Path to sustainable energy consumption: The possibility of substituting renewable energy for non-renewable energy," Energy, Elsevier, vol. 228(C).
    10. Yang, Jinshui & Peng, Chaoyi & Xiao, Jiayu & Zeng, Jingcheng & Yuan, Yun, 2012. "Application of videometric technique to deformation measurement for large-scale composite wind turbine blade," Applied Energy, Elsevier, vol. 98(C), pages 292-300.
    11. ASM Shihavuddin & Xiao Chen & Vladimir Fedorov & Anders Nymark Christensen & Nicolai Andre Brogaard Riis & Kim Branner & Anders Bjorholm Dahl & Rasmus Reinhold Paulsen, 2019. "Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis," Energies, MDPI, vol. 12(4), pages 1-15, February.
    12. Madi, Ezieddin & Pope, Kevin & Huang, Weimin & Iqbal, Tariq, 2019. "A review of integrating ice detection and mitigation for wind turbine blades," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 269-281.
    13. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    14. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    15. Lijun Zhang & Kai Liu & Yufeng Wang & Zachary Bosire Omariba, 2018. "Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier," Energies, MDPI, vol. 11(10), pages 1-15, September.
    16. Gustave P. Corten & Herman F. Veldkamp, 2001. "Insects can halve wind-turbine power," Nature, Nature, vol. 412(6842), pages 41-42, July.
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    Cited by:

    1. Chunsheng Hu & Yong Zhao & Fangjuan Cheng & Zhiping Li, 2023. "Multi-Object Detection Algorithm in Wind Turbine Nacelles Based on Improved YOLOX-Nano," Energies, MDPI, vol. 16(3), pages 1-13, January.
    2. Yuan Yao & Guozhong Wang & Jinhui Fan, 2023. "WT-YOLOX: An Efficient Detection Algorithm for Wind Turbine Blade Damage Based on YOLOX," Energies, MDPI, vol. 16(9), pages 1-15, April.
    3. Małgorzata Jastrzębska, 2022. "Installation’s Conception in the Field of Renewable Energy Sources for the Needs of the Silesian Botanical Garden," Energies, MDPI, vol. 15(18), pages 1-28, September.

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