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A Comprehensive Analysis of Wind Turbine Blade Damage

Author

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  • Dimitris Al. Katsaprakakis

    (Power Plant Synthesis Laboratory, Department of Mechanical Engineering, Hellenic Mediterranean University, Estavromenos, 714 10 Heraklion, Crete, Greece)

  • Nikos Papadakis

    (Power Plant Synthesis Laboratory, Department of Mechanical Engineering, Hellenic Mediterranean University, Estavromenos, 714 10 Heraklion, Crete, Greece)

  • Ioannis Ntintakis

    (Power Plant Synthesis Laboratory, Department of Mechanical Engineering, Hellenic Mediterranean University, Estavromenos, 714 10 Heraklion, Crete, Greece)

Abstract

The scope of this article is to review the potential causes that can lead to wind turbine blade failures, assess their significance to a turbine’s performance and secure operation and summarize the techniques proposed to prevent these failures and eliminate their consequences. Damage to wind turbine blades can be induced by lightning, fatigue loads, accumulation of icing on the blade surfaces and the exposure of blades to airborne particulates, causing so-called leading edge erosion. The above effects can lead to damage ranging from minor outer surface erosion to total destruction of the blade. All potential causes of damage to wind turbine blades strongly depend on the surrounding environment and climate conditions. Consequently, the selection of an installation site with favourable conditions is the most effective measure to minimize the possibility of blade damage. Otherwise, several techniques and methods have already been applied or are being developed to prevent blade damage, aiming to reduce damage risk if not able to eliminate it. The combined application of damage prevention strategies with a SCADA system is the optimal approach to adequate treatment.

Suggested Citation

  • Dimitris Al. Katsaprakakis & Nikos Papadakis & Ioannis Ntintakis, 2021. "A Comprehensive Analysis of Wind Turbine Blade Damage," Energies, MDPI, vol. 14(18), pages 1-31, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5974-:d:639555
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    References listed on IDEAS

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    1. Fuentes, R. & Dwyer-Joyce, R.S. & Marshall, M.B. & Wheals, J. & Cross, E.J., 2020. "Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling," Renewable Energy, Elsevier, vol. 147(P1), pages 776-797.
    2. Rodrigues, R.B. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "Protection of wind energy systems against the indirect effects of lightning," Renewable Energy, Elsevier, vol. 36(11), pages 2888-2896.
    3. Sima Rastayesh & Lijia Long & John Dalsgaard Sørensen & Sebastian Thöns, 2019. "Risk Assessment and Value of Action Analysis for Icing Conditions of Wind Turbines Close to Highways," Energies, MDPI, vol. 12(14), pages 1-15, July.
    4. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    5. Yang Zhao & Xi Wang & Qibin Zhou & Zhenxing Wang & Xiaoyan Bian, 2020. "Numerical Study of Lightning Protection of Wind Turbine Blade with De-Icing Electrical Heating System," Energies, MDPI, vol. 13(3), pages 1-11, February.
    6. James W. K. Nash & Iasonas Zekos & Margaret M. Stack, 2021. "Mapping of Meteorological Observations over the Island of Ireland to Enhance the Understanding and Prediction of Rain Erosion in Wind Turbine Blades," Energies, MDPI, vol. 14(15), pages 1-34, July.
    7. Jiangyan Yan & Guozheng Wang & Qingmin Li & Li Zhang & Joseph D. Yan & Chun Chen & Zhiyang Fang, 2017. "A Comparative Study on Damage Mechanism of Sandwich Structures with Different Core Materials under Lightning Strikes," Energies, MDPI, vol. 10(10), pages 1-14, October.
    8. Slot, H.M. & Gelinck, E.R.M. & Rentrop, C. & van der Heide, E., 2015. "Leading edge erosion of coated wind turbine blades: Review of coating life models," Renewable Energy, Elsevier, vol. 80(C), pages 837-848.
    9. Charlotte Bay Hasager & Flemming Vejen & Witold Robert Skrzypiński & Anna-Maria Tilg, 2021. "Rain Erosion Load and Its Effect on Leading-Edge Lifetime and Potential of Erosion-Safe Mode at Wind Turbines in the North Sea and Baltic Sea," Energies, MDPI, vol. 14(7), pages 1-24, April.
    10. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
    11. Matthias Schramm & Hamid Rahimi & Bernhard Stoevesandt & Kim Tangager, 2017. "The Influence of Eroded Blades on Wind Turbine Performance Using Numerical Simulations," Energies, MDPI, vol. 10(9), pages 1-15, September.
    12. Katsaprakakis, Dimitris Al. & Christakis, Dimitris G., 2016. "The exploitation of electricity production projects from Renewable Energy Sources for the social and economic development of remote communities. The case of Greece: An example to avoid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 341-349.
    13. Daniliuk, Vladislav & Xu, Yuanming & Liu, Ruobing & He, Tianpeng & Wang, Xi, 2020. "Ultrasonic de-icing of wind turbine blades: Performance comparison of perspective transducers," Renewable Energy, Elsevier, vol. 145(C), pages 2005-2018.
    14. Kong, C. & Bang, J. & Sugiyama, Y., 2005. "Structural investigation of composite wind turbine blade considering various load cases and fatigue life," Energy, Elsevier, vol. 30(11), pages 2101-2114.
    15. Guo, Jihong & Liu, Chao & Cao, Jinfeng & Jiang, Dongxiang, 2021. "Damage identification of wind turbine blades with deep convolutional neural networks," Renewable Energy, Elsevier, vol. 174(C), pages 122-133.
    16. Xiyun Yang & Tianze Ye & Qile Wang & Zhun Tao, 2020. "Diagnosis of Blade Icing Using Multiple Intelligent Algorithms," Energies, MDPI, vol. 13(11), pages 1-15, June.
    17. 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.
    18. Sudhakar Gantasala & Narges Tabatabaei & Michel Cervantes & Jan-Olov Aidanpää, 2019. "Numerical Investigation of the Aeroelastic Behavior of a Wind Turbine with Iced Blades," Energies, MDPI, vol. 12(12), pages 1-24, June.
    19. Yi Yang & John Dalsgaard Sørensen, 2019. "Cost-Optimal Maintenance Planning for Defects on Wind Turbine Blades," Energies, MDPI, vol. 12(6), pages 1-16, March.
    20. Xishan Wen & Lu Qu & Yu Wang & Xiaoyue Chen & Lei Lan & Tianjun Si & Jianwei Xu, 2016. "Effect of Wind Turbine Blade Rotation on Triggering Lightning: An Experimental Study," Energies, MDPI, vol. 9(12), pages 1-15, December.
    21. Habibi, Hossein & Cheng, Liang & Zheng, Haitao & Kappatos, Vassilios & Selcuk, Cem & Gan, Tat-Hean, 2015. "A dual de-icing system for wind turbine blades combining high-power ultrasonic guided waves and low-frequency forced vibrations," Renewable Energy, Elsevier, vol. 83(C), pages 859-870.
    22. 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.
    23. Takanori Uchida & Yasushi Kawashima, 2019. "New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence," Energies, MDPI, vol. 12(13), pages 1-27, July.
    24. Nikki Kluskens & Véronique Vasseur & Rowan Benning, 2019. "Energy Justice as Part of the Acceptance of Wind Energy: An Analysis of Limburg in The Netherlands," Energies, MDPI, vol. 12(22), pages 1-19, November.
    25. Sudhakar Gantasala & Jean-Claude Luneno & Jan-Olov Aidanpää, 2016. "Influence of Icing on the Modal Behavior of Wind Turbine Blades," Energies, MDPI, vol. 9(11), pages 1-14, October.
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    5. Xiaowen Song & Zhitai Xing & Yan Jia & Xiaojuan Song & Chang Cai & Yinan Zhang & Zekun Wang & Jicai Guo & Qingan Li, 2022. "Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage," Energies, MDPI, vol. 15(20), pages 1-17, October.

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