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Fatigue reliability assessment of offshore wind turbines with stochastic availability

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  • Horn, Jan-Tore
  • Leira, Bernt J.

Abstract

In this paper, the impact on lifetime estimation of an offshore wind turbine by introducing a stochastic model for the availability is investigated. Offshore bottom-fixed wind turbines typically have an average downtime of 4–10% due to e.g. grid- or mechanical failures including a potentially long response time for recovery. During the non-operational conditions, the fatigue damage in the foundation is accumulating significantly faster. Designing the wind farm based on a conservative downtime fraction will lead to design conservatism with respect to the foundation, which will be quantified in this paper using a structural reliability analysis.

Suggested Citation

  • Horn, Jan-Tore & Leira, Bernt J., 2019. "Fatigue reliability assessment of offshore wind turbines with stochastic availability," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018301492
    DOI: 10.1016/j.ress.2019.106550
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    References listed on IDEAS

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    1. Scheu, Matti Niclas & Kolios, Athanasios & Fischer, Tim & Brennan, Feargal, 2017. "Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 28-39.
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    Cited by:

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    4. Hegseth, John Marius & Bachynski, Erin E. & Leira, Bernt J., 2021. "Effect of environmental modelling and inspection strategy on the optimal design of floating wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Javier Serrano González & Manuel Burgos Payán & Jesús Manuel Riquelme Santos & Ángel Gaspar González Rodríguez, 2021. "Optimal Micro-Siting of Weathervaning Floating Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-19, February.
    6. Liu, Fuxiu & Li, Zhaojun & Liang, Minglang & Zhao, Binjian & Ding, Jiang, 2023. "Prediction method of non-stationary random vibration fatigue reliability of turbine runner blade based on transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    8. Thapa, Mishal & Missoum, Samy, 2022. "Uncertainty quantification and global sensitivity analysis of composite wind turbine blades," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Shittu, Abdulhakim Adeoye & Mehmanparast, Ali & Hart, Phil & Kolios, Athanasios, 2021. "Comparative study between S-N and fracture mechanics approach on reliability assessment of offshore wind turbine jacket foundations," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Wang, L. & Kolios, A. & Liu, X. & Venetsanos, D. & Rui, C., 2022. "Reliability of offshore wind turbine support structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
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    12. Ramezani, Mahyar & Choe, Do-Eun & Heydarpour, Khashayar & Koo, Bonjun, 2023. "Uncertainty models for the structural design of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).

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