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Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability

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  • Scheu, Matti Niclas
  • Kolios, Athanasios
  • Fischer, Tim
  • Brennan, Feargal

Abstract

Offshore wind turbine reliability, one of the industry's biggest sources of uncertainty, is the focus of the present paper. Specifically the impact of uncertain component failure distributions at constant failure rates has been investigated with respect to its implications for wind farm availability. A fully probabilistic offshore wind simulation model has been applied to quantify results; effects shown in this paper underline the significant impact that failure probability distributions have on asset performance evaluation. It was found that wind farm availability numbers may vary in the range up to 20% just by changing the distributions of failure to a different pattern; in particular those scenarios in which extensive failure accumulation occurred led to significant losses in production. Results are interpreted and discussed mainly from the viewpoint of an offshore wind farm developer, owner and operator, with implications underlined for application in state-of-the-art offshore wind O&M (Operations and Maintenance) models and simulation tools.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:168:y:2017:i:c:p:28-39
    DOI: 10.1016/j.ress.2017.05.021
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    References listed on IDEAS

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    Cited by:

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    5. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
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    7. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).
    8. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    9. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2018. "A lifecycle techno-economic model of offshore wind energy for different entry and exit instances," Applied Energy, Elsevier, vol. 221(C), pages 406-424.
    10. Yürüşen, Nurseda Y. & Rowley, Paul N. & Watson, Simon J. & Melero, Julio J., 2020. "Automated wind turbine maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    11. Hans Olav Vogt Myklebust & Jo Eidsvik & Iver Bakken Sperstad & Debarun Bhattacharjya, 2020. "Value of Information Analysis for Complex Simulator Models: Application to Wind Farm Maintenance," Decision Analysis, INFORMS, vol. 17(2), pages 134-153, June.
    12. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    13. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    14. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    15. 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.
    16. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2023. "A closed-loop maintenance strategy for offshore wind farms: Incorporating dynamic wind farm states and uncertainty-awareness in decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    17. Soszyńska-Budny Joanna & Chmielewski Mariusz & Pioch Joanna, 2023. "Reliability of Renewable Power Generation using the Example of Offshore Wind Farms," Folia Oeconomica Stetinensia, Sciendo, vol. 23(1), pages 228-245, June.
    18. 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).
    19. Neves-Moreira, Fábio & Veldman, Jasper & Teunter, Ruud H., 2021. "Service operation vessels for offshore wind farm maintenance: Optimal stock levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    20. Wu, Shaomin & Do, Phuc, 2017. "Editorial," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 1-3.
    21. 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).

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