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Offshore wind turbine risk quantification/evaluation under extreme environmental conditions

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  • Taflanidis, Alexandros A.
  • Loukogeorgaki, Eva
  • Angelides, Demos C.

Abstract

A simulation-based framework is discussed in this paper for quantification/evaluation of risk and development of automated risk assessment tools, focusing on applications to offshore wind turbines under extreme environmental conditions. The framework is founded on a probabilistic characterization of the uncertainty in the models for the excitation, the turbine and its performance. Risk is then quantified as the expected value of some risk consequence measure over the probability distributions considered for the uncertain model parameters. Stochastic simulation is proposed for the risk assessment, corresponding to the evaluation of some associated probabilistic integral quantifying risk, as it allows for the adoption of comprehensive computational models for describing the dynamic turbine behavior. For improvement of the computational efficiency, a surrogate modeling approach is introduced based on moving least squares response surface approximations. The assessment is also extended to a probabilistic sensitivity analysis that identifies the importance of each of the uncertain model parameters, i.e. risk factors, towards the total risk as well as towards each of the failure modes contributing to this risk. The versatility and computational efficiency of the advocated approaches is finally exploited to support the development of standalone risk assessment applets for automated implementation of the probabilistic risk quantification/assessment.

Suggested Citation

  • Taflanidis, Alexandros A. & Loukogeorgaki, Eva & Angelides, Demos C., 2013. "Offshore wind turbine risk quantification/evaluation under extreme environmental conditions," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 19-32.
    • Handle: RePEc:eee:reensy:v:115:y:2013:i:c:p:19-32
    DOI: 10.1016/j.ress.2013.02.003
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    Cited by:

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    3. Jin, Xin & Zhang, Zhaolong & Shi, Xiaoqiang & Ju, Wenbin, 2014. "A review on wind power industry and corresponding insurance market in China: Current status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 1069-1082.
    4. Pokhrel, Jharna & Seo, Junwon, 2021. "Statistical model for fragility estimates of offshore wind turbines subjected to aero-hydro dynamic loads," Renewable Energy, Elsevier, vol. 163(C), pages 1495-1507.
    5. Hong, H.P., 2013. "Selection of regressand for fitting the extreme value distributions using the ordinary, weighted and generalized least-squares methods," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 71-80.
    6. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    7. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).
    8. Lee, Yeon-Seung & Choi, Byung-Lyul & Lee, Ji Hyun & Kim, Soo Young & Han, Soonhung, 2014. "Reliability-based design optimization of monopile transition piece for offshore wind turbine system," Renewable Energy, Elsevier, vol. 71(C), pages 729-741.
    9. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    10. Hu, Xiaonong & Fang, Genshen & Yang, Jiayu & Zhao, Lin & Ge, Yaojun, 2023. "Simplified models for uncertainty quantification of extreme events using Monte Carlo technique," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Charlton, T.S. & Rouainia, M., 2022. "Geotechnical fragility analysis of monopile foundations for offshore wind turbines in extreme storms," Renewable Energy, Elsevier, vol. 182(C), pages 1126-1140.

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