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Progress and challenges on blade load research of large-scale wind turbines

Author

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  • Dai, Juchuan
  • Li, Mimi
  • Chen, Huanguo
  • He, Tao
  • Zhang, Fan

Abstract

Load identification is the premise of wind turbine blade design and control. In wind farms, due to the lack of a thorough understanding of the blade loads, catastrophic accidents occur occasionally, and the causes of some accidents cannot be reasonably explained. To further promote the depth of blade load research and application, the development of the blade industry, load theory, load measurement, and load control of large-scale Horizontal Axis Wind Turbines (HAWTs) are comprehensively reviewed in this paper. The key issues and the corresponding progress involved in the blade research and application are discussed in detail, covering aerodynamic load calculation and analysis, load measurement in both laboratory and field environments, and load control of wind turbine blades. Additionally, the main matters needing attention in the blade research and application in the future are outlined, including more accurate and faster load calculation, strain perception and load mapping in the field, reconstruction of historical service load, and time-history optimization of the load control algorithm. Some effective countermeasures, e.g., reconstructing the historical service loads on wind turbine blades with the help of both blade load sensors and the Supervisory Control and Data Acquisition (SCADA) system, are also proposed.

Suggested Citation

  • Dai, Juchuan & Li, Mimi & Chen, Huanguo & He, Tao & Zhang, Fan, 2022. "Progress and challenges on blade load research of large-scale wind turbines," Renewable Energy, Elsevier, vol. 196(C), pages 482-496.
    • Handle: RePEc:eee:renene:v:196:y:2022:i:c:p:482-496
    DOI: 10.1016/j.renene.2022.07.017
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    as
    1. Wood, D.H. & Okulov, V.L. & Bhattacharjee, D., 2016. "Direct calculation of wind turbine tip loss," Renewable Energy, Elsevier, vol. 95(C), pages 269-276.
    2. Chen, Z.J. & Stol, K.A. & Mace, B.R., 2017. "Wind turbine blade optimisation with individual pitch and trailing edge flap control," Renewable Energy, Elsevier, vol. 103(C), pages 750-765.
    3. Schubel, P.J. & Crossley, R.J. & Boateng, E.K.G. & Hutchinson, J.R., 2013. "Review of structural health and cure monitoring techniques for large wind turbine blades," Renewable Energy, Elsevier, vol. 51(C), pages 113-123.
    4. Johansen, Katinka, 2021. "Blowing in the wind: A brief history of wind energy and wind power technologies in Denmark," Energy Policy, Elsevier, vol. 152(C).
    5. Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
    6. Rodriguez, Steven N. & Jaworski, Justin W., 2019. "Strongly-coupled aeroelastic free-vortex wake framework for floating offshore wind turbine rotors. Part 1: Numerical framework," Renewable Energy, Elsevier, vol. 141(C), pages 1127-1145.
    7. Dai, J.C. & Hu, Y.P. & Liu, D.S. & Long, X., 2011. "Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model," Renewable Energy, Elsevier, vol. 36(3), pages 1095-1104.
    8. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    9. Bangga, Galih & Lutz, Thorsten, 2021. "Aerodynamic modeling of wind turbine loads exposed to turbulent inflow and validation with experimental data," Energy, Elsevier, vol. 223(C).
    10. Hou, Peng & Hu, Weihao & Soltani, Mohsen & Chen, Cong & Chen, Zhe, 2017. "Combined optimization for offshore wind turbine micro siting," Applied Energy, Elsevier, vol. 189(C), pages 271-282.
    11. Tabassum-Abbasi, & Premalatha, M. & Abbasi, Tasneem & Abbasi, S.A., 2014. "Wind energy: Increasing deployment, rising environmental concerns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 270-288.
    12. Shen, Xin & Zhu, Xiaocheng & Du, Zhaohui, 2011. "Wind turbine aerodynamics and loads control in wind shear flow," Energy, Elsevier, vol. 36(3), pages 1424-1434.
    13. Sun, Zhenye & Chen, Jin & Shen, Wen Zhong & Zhu, Wei Jun, 2016. "Improved blade element momentum theory for wind turbine aerodynamic computations," Renewable Energy, Elsevier, vol. 96(PA), pages 824-831.
    14. Rezaeiha, Abdolrahim & Kalkman, Ivo & Blocken, Bert, 2017. "Effect of pitch angle on power performance and aerodynamics of a vertical axis wind turbine," Applied Energy, Elsevier, vol. 197(C), pages 132-150.
    15. 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.
    16. Dai, Juchuan & Yang, Xin & Wen, Li, 2018. "Development of wind power industry in China: A comprehensive assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 156-164.
    17. Dai, Juchuan & Liu, Deshun & Wen, Li & Long, Xin, 2016. "Research on power coefficient of wind turbines based on SCADA data," Renewable Energy, Elsevier, vol. 86(C), pages 206-215.
    18. Lanzafame, R. & Messina, M., 2007. "Fluid dynamics wind turbine design: Critical analysis, optimization and application of BEM theory," Renewable Energy, Elsevier, vol. 32(14), pages 2291-2305.
    19. Zheng, Chong Wei & Li, Chong Yin & Pan, Jing & Liu, Ming Yang & Xia, Lin Lin, 2016. "An overview of global ocean wind energy resource evaluations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1240-1251.
    20. Yang, Hua & Shen, Wenzhong & Xu, Haoran & Hong, Zedong & Liu, Chao, 2014. "Prediction of the wind turbine performance by using BEM with airfoil data extracted from CFD," Renewable Energy, Elsevier, vol. 70(C), pages 107-115.
    Full references (including those not matched with items on IDEAS)

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