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A model for availability growth with application to new generation offshore wind farms

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  • Zitrou, Athena
  • Bedford, Tim
  • Walls, Lesley

Abstract

A model for availability growth is developed to capture the effect of systemic risk prior to construction of a complex system. The model has been motivated by new generation offshore wind farms where investment decisions need to be taken before test and operational data are available. We develop a generic model to capture the systemic risks arising from innovation in evolutionary system designs. By modelling the impact of major and minor interventions to mitigate weaknesses and to improve the failure and restoration processes of subassemblies, we are able to measure the growth in availability performance of the system. We describe the choices made in modelling our particular industrial setting using an example for a typical UK Round III offshore wind farm. We obtain point estimates of the expected availability having populated the simulated model using appropriate judgemental and empirical data. We show the relative impact of modelling systemic risk on system availability performance in comparison with estimates obtained from typical system availability modelling assumptions used in offshore wind applications. While modelling growth in availability is necessary for meaningful decision support in developing complex systems such as offshore wind farms, we also discuss the relative value of explicitly articulating epistemic uncertainties.

Suggested Citation

  • Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2016. "A model for availability growth with application to new generation offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 83-94.
    • Handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:83-94
    DOI: 10.1016/j.ress.2015.12.004
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    References listed on IDEAS

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    1. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    2. Wang, Wenbin, 2012. "An overview of the recent advances in delay-time-based maintenance modelling," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 165-178.
    3. J. Ansell & L. Walls & J. Quigley, 1999. "Achieving growth in reliability," Annals of Operations Research, Springer, vol. 91(0), pages 11-24, January.
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    Cited by:

    1. Iain Dinwoodie & David McMillan & Iraklis Lazakis & Yalcin Dalgic & Matthew Revie, 2018. "On modeling insights for emerging engineering problems: A case study on the impact of climate uncertainty on the operational performance of offshore wind farms," Journal of Risk and Reliability, , vol. 232(5), pages 524-532, October.
    2. Chun Su & Longfei Cheng, 2018. "An availability-based warranty policy considering preventive maintenance and learning effects," Journal of Risk and Reliability, , vol. 232(6), pages 576-586, December.
    3. Niemi, Arto & Skobiej, Bartosz & Kulev, Nikolai & Sill Torres, Frank, 2024. "Modeling offshore wind farm disturbances and maintenance service responses within the scope of resilience," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Shafiee, Mahmood & Sørensen, John Dalsgaard, 2019. "Maintenance optimization and inspection planning of wind energy assets: Models, methods and strategies," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    5. Tang, Huakang & Wang, Honglei & Li, Chengjiang, 2025. "Time-varying cost modeling and maintenance strategy optimization of plateau wind turbines considering degradation states," Applied Energy, Elsevier, vol. 377(PA).
    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.

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