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Smart fatigue load control on the large-scale wind turbine blades using different sensing signals

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  • Zhang, Mingming
  • Tan, Bin
  • Xu, Jianzhong

Abstract

This paper presented a numerical study on the smart fatigue load control of a large-scale wind turbine blade. Three typical control strategies, with sensing signals from flapwise acceleration, root moment and tip deflection of the blade, respectively, were mainly investigated on our newly developed aero-servo-elastic platform. It was observed that the smart control greatly modified in-phased flow-blade interaction into an anti-phased one at primary 1P mode, significantly enhancing the damping of the fluid-structure system and subsequently contributing to effectively attenuated fatigue loads on the blade, drive-chain components and tower. The aero-elastic physics behind the strategy based on the flapwise root moment, with stronger dominant load information and higher signal-to-noise ratio, was more drastic, and thus outperformed the other two strategies, leading to the maximum reduction percentages of the fatigue load within a range of 12.0–22.5%, in contrast to the collective pitch control method. The finding pointed to a crucial role the sensing signal played in the smart blade control. In addition, the performances within region III were much better than those within region II, exhibiting the benefit of the smart rotor control since most of the fatigue damage was believed to be accumulated beyond the rated wind speed.

Suggested Citation

  • Zhang, Mingming & Tan, Bin & Xu, Jianzhong, 2016. "Smart fatigue load control on the large-scale wind turbine blades using different sensing signals," Renewable Energy, Elsevier, vol. 87(P1), pages 111-119.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:111-119
    DOI: 10.1016/j.renene.2015.10.011
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    References listed on IDEAS

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    1. Cooperman, Aubryn & Martinez, Marcias, 2015. "Load monitoring for active control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 189-201.
    2. Zhang, Mingming & Yu, Wei & Xu, Jianzhong, 2014. "Aerodynamic physics of smart load control for wind turbine due to extreme wind shear," Renewable Energy, Elsevier, vol. 70(C), pages 204-210.
    3. Zhang, Mingming & Tan, Bin & Xu, Jianzhong, 2015. "Parameter study of sizing and placement of deformable trailing edge flap on blade fatigue load reduction," Renewable Energy, Elsevier, vol. 77(C), pages 217-226.
    4. Wei Yu & Ming Ming Zhang & Jian Zhong Xu, 2012. "Effect of Smart Rotor Control Using a Deformable Trailing Edge Flap on Load Reduction under Normal and Extreme Turbulence," Energies, MDPI, vol. 5(9), pages 1-19, September.
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    Cited by:

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    12. Zhang, Mingming & Li, Xin & Tong, Jingxin & Xu, Jianzhong, 2020. "Load control of floating wind turbine on a Tension-Leg-Platform subject to extreme wind condition," Renewable Energy, Elsevier, vol. 151(C), pages 993-1007.
    13. Ai, Qing & Weaver, Paul M. & Barlas, Thanasis K. & Olsen, Anders S. & Madsen, Helge A. & Andersen, Tom L., 2019. "Field testing of morphing flaps on a wind turbine blade using an outdoor rotating rig," Renewable Energy, Elsevier, vol. 133(C), pages 53-65.
    14. Li, Jiale & Wang, Xuefei & Guo, Yuan & Yu, Xiong Bill, 2020. "The loading behavior of innovative monopile foundations for offshore wind turbine based on centrifuge experiments," Renewable Energy, Elsevier, vol. 152(C), pages 1109-1120.
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    17. Watson, Simon & Moro, Alberto & Reis, Vera & Baniotopoulos, Charalampos & Barth, Stephan & Bartoli, Gianni & Bauer, Florian & Boelman, Elisa & Bosse, Dennis & Cherubini, Antonello & Croce, Alessandro , 2019. "Future emerging technologies in the wind power sector: A European perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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