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An efficient rotational sampling method of wind fields for wind turbine blade fatigue analysis

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  • Chen, Jianbing
  • Song, Yupeng
  • Peng, Yongbo
  • Nielsen, Søren R.K.
  • Zhang, Zili

Abstract

Both the operational and ultimate load conditions should be considered in the structural design and reliability assessment of wind turbine systems. In the operational condition, the fatigue load experienced by wind turbine blades is of great concern in design which highly relies upon the rotor’s rotation. Three kinds of methods have been developed to explore the rotational sampling effect of wind speeds on wind turbine blades, which, however, are somewhat inconvenient in practical applications. In view of the recent developments in wind field simulation, a novel rotational sampling method allowing for the analytical expression of fluctuating wind speeds on rotating blades is proposed in the present paper. In contrast to the existing methods, the proposed method circumvents the decomposition of cross power spectrum density (PSD) matrix and the interpolation in spatial and temporal dimensions. In particular, a closed-form expression of the rotational sampling spectrum is provided, thereby the mechanism of transfer of turbulent kinetic energy in frequency domain is quantitatively revealed. For illustrative purposes, fatigue analysis of the blades of a 5-MW offshore wind turbine is carried out, demonstrating the non-negligible influence of the rotational sampling on the fatigue load of blades and the competitive efficiency of the proposed method.

Suggested Citation

  • Chen, Jianbing & Song, Yupeng & Peng, Yongbo & Nielsen, Søren R.K. & Zhang, Zili, 2020. "An efficient rotational sampling method of wind fields for wind turbine blade fatigue analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2170-2187.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2170-2187
    DOI: 10.1016/j.renene.2019.08.015
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    Cited by:

    1. Song, Yupeng & Basu, Biswajit & Zhang, Zili & Sørensen, John Dalsgaard & Li, Jie & Chen, Jianbing, 2021. "Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method," Renewable Energy, Elsevier, vol. 168(C), pages 991-1014.
    2. Ren, Xiaojun & Wu, Yongtang & Hao, Dongmin & Liu, Guoxu & Zafetti, Nicholas, 2021. "Analysis of the performance of the multi-objective hybrid hydropower-photovoltaic-wind system to reduce variance and maximum power generation by developed owl search algorithm," Energy, Elsevier, vol. 231(C).
    3. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.

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