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Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions

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  • Song, Yupeng
  • Sun, Tao
  • Zhang, Zili

Abstract

The fatigue reliability-based design is critical for the structural safety of floating offshore wind turbines (FOWTs). In principle, infinite potential load conditions that the FOWT may experience should be considered in fatigue assessment, while in practice only limited number of load conditions are used, which inevitably induces uncertainty to fatigue estimation. However, to this uncertainty, little attention has been paid. In this study, the fatigue reliability of a FOWT is investigated with a focus on this uncertainty. The C-vine copula method is adopted to model concurrent wind and wave conditions, and the probability-based sampling method is employed to determine the load conditions used for fatigue analysis. This uncertainty is assessed quantitatively via bootstrap method based on numerous simulations. The Gaussian random variable can be used to describe the uncertainty, and the standard variation is proportional to the −0.5 power of the number of load conditions. The fatigue reliability evaluation model of FOWT is established considering this uncertainty, and sensitivity analysis is performed to assess the influence of different random variables. The results indicate that the effect of this uncertainty is comparable to some other uncertainties in certain cases, and should be paid attention to in practice.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:212:y:2023:i:c:p:570-588
    DOI: 10.1016/j.renene.2023.05.070
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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. Li, Xuan & Zhang, Wei, 2020. "Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions," Renewable Energy, Elsevier, vol. 159(C), pages 570-584.
    3. Dong, Wenbin & Moan, Torgeir & Gao, Zhen, 2012. "Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 11-27.
    4. Micallef, Daniel & Rezaeiha, Abdolrahim, 2021. "Floating offshore wind turbine aerodynamics: Trends and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    5. Emanuele Borgonovo & William Castaings & Stefano Tarantola, 2011. "Moment Independent Importance Measures: New Results and Analytical Test Cases," Risk Analysis, John Wiley & Sons, vol. 31(3), pages 404-428, March.
    6. 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.
    7. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    8. Slot, René M.M. & Sørensen, John D. & Sudret, Bruno & Svenningsen, Lasse & Thøgersen, Morten L., 2020. "Surrogate model uncertainty in wind turbine reliability assessment," Renewable Energy, Elsevier, vol. 151(C), pages 1150-1162.
    9. Li, Xuan & Zhang, Wei, 2020. "Long-term assessment of a floating offshore wind turbine under environmental conditions with multivariate dependence structures," Renewable Energy, Elsevier, vol. 147(P1), pages 764-775.
    10. 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.
    11. Chen, Jianbing & Liu, Zenghui & Song, Yupeng & Peng, Yongbo & Li, Jie, 2022. "Experimental study on dynamic responses of a spar-type floating offshore wind turbine," Renewable Energy, Elsevier, vol. 196(C), pages 560-578.
    12. Chi-Yu Chian & Yi-Qing Zhao & Tsung-Yueh Lin & Bryan Nelson & Hsin-Haou Huang, 2018. "Comparative Study of Time-Domain Fatigue Assessments for an Offshore Wind Turbine Jacket Substructure by Using Conventional Grid-Based and Monte Carlo Sampling Methods," Energies, MDPI, vol. 11(11), pages 1-17, November.
    13. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
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