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Towards resilience of offshore wind farms: A framework and application to asset integrity management

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  • Liu, Min
  • Qin, Jianjun
  • Lu, Da-Gang
  • Zhang, Wei-Heng
  • Zhu, Jiang-Sheng
  • Faber, Michael Havbro

Abstract

This paper develops a general probabilistic framework for resilience modeling and analysis of offshore wind farm (OWF), and illustrates how such a framework may be implemented within the modeling techniques and tools commonly applied in the industry. Based on this framework the significance of prevailing uncertainties and the effects of different decision alternatives relevant in the context of asset integrity management (AIM) are studied and discussed. In the framework, OWFs are modeled as system-of-systems by a hierarchical model where the life-cycle performances of each system, as well as the dependencies between these systems, are represented probabilistically. The quantification of resilience is undertaken based on a scenario-based modeling of life cycle benefits and costs in which resilience failure is defined as the exhaustion of the economic capacity accumulated by the system over time. Moreover, this paper introduces resilience-informed decision-making for OWF in the context of AIM. The proposed framework is applied to the OWFs populated with NREL 5MW offshore wind turbines (OWTs). Events of typhoon-induced waves and winds are considered as the two random environmental load processes affecting the OWF’s dynamic responses and for which their resilience performances are carried out. Finally, the resilience performances of the OWFs are studied and discussed for a range of decision alternatives relevant to AIM.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922007619
    DOI: 10.1016/j.apenergy.2022.119429
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    Cited by:

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    2. Pan, Yue & Qin, Jianjun, 2022. "A novel probabilistic modeling framework for wind speed with highlight of extremes under data discrepancy and uncertainty," Applied Energy, Elsevier, vol. 326(C).

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