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Influence of the control system on wind turbine loads during power production in extreme turbulence: Structural reliability

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  • Abdallah, I.
  • Natarajan, A.
  • Sørensen, J.D.

Abstract

The wind energy industry is continuously researching better computational models of wind inflow and turbulence to predict extreme loading (the nature of randomness) and their corresponding probability of occurrence. Sophisticated load alleviation control systems are increasingly being designed and deployed to specifically reduce the adverse effects of extreme load events resulting in lighter structures. The main objective herein is to show that despite large uncertainty in the extreme turbulence models, advanced load alleviation control systems yield both a reduction in magnitude and scatter of the extreme loads which in turn translates in a change in the shape of the annual maximum load distribution function resulting in improved structural reliability. Using a probabilistic loads extrapolation approach and the first order reliability method, a large multi-megawatt wind turbine blade and tower structural reliability are assessed when the extreme turbulence model is uncertain. The structural reliability is assessed for the wind turbine when three configurations of an industrial grade load alleviation control system of increasing complexity and performance are used. The load alleviation features include a cyclic pitch, individual pitch, static thrust limiter, condition based thrust limiter and an active tower vibration damper. We show that large uncertainties in the extreme turbulence model can be mitigated and significantly reduced while maintaining an acceptable structural reliability level when advanced load alleviation control systems are used. We end by providing a rational comparison between the long term loads extrapolation method and the environmental contour method for the three control configurations.

Suggested Citation

  • Abdallah, I. & Natarajan, A. & Sørensen, J.D., 2016. "Influence of the control system on wind turbine loads during power production in extreme turbulence: Structural reliability," Renewable Energy, Elsevier, vol. 87(P1), pages 464-477.
  • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:464-477
    DOI: 10.1016/j.renene.2015.10.044
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    Citations

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    Cited by:

    1. Bracale, Antonio & Carpinelli, Guido & De Falco, Pasquale, 2017. "A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization," Renewable Energy, Elsevier, vol. 113(C), pages 1366-1377.
    2. Wu, Guangxing & Zhang, Chaoyu & Cai, Chang & Yang, Ke & Shi, Kezhong, 2020. "Uncertainty prediction on the angle of attack of wind turbine blades based on the field measurements," Energy, Elsevier, vol. 200(C).
    3. Murcia, Juan Pablo & Réthoré, Pierre-Elouan & Dimitrov, Nikolay & Natarajan, Anand & Sørensen, John Dalsgaard & Graf, Peter & Kim, Taeseong, 2018. "Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates," Renewable Energy, Elsevier, vol. 119(C), pages 910-922.
    4. Dai, S.F. & Liu, H.J. & Chu, Y.J. & Lam, H.F. & Peng, H.Y., 2022. "Impact of corner modification on wind characteristics and wind energy potential over flat roofs of tall buildings," Energy, Elsevier, vol. 241(C).
    5. W. Dheelibun Remigius & Anand Natarajan, 2022. "A review of wind turbine drivetrain loads and load effects for fixed and floating wind turbines," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(1), January.
    6. Shao, Yizhe & Liu, Jie, 2024. "Uncertainty quantification for dynamic responses of offshore wind turbine based on manifold learning," Renewable Energy, Elsevier, vol. 222(C).
    7. Dong, Liang & Lio, Wai Hou & Pirrung, Georg Raimund, 2021. "Analysis and design of an adaptive turbulence-based controller for wind turbines," Renewable Energy, Elsevier, vol. 178(C), pages 730-744.
    8. Zhiyu Jiang & Weifei Hu & Wenbin Dong & Zhen Gao & Zhengru Ren, 2017. "Structural Reliability Analysis of Wind Turbines: A Review," Energies, MDPI, vol. 10(12), pages 1-25, December.

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