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A multi-faceted opportunistic-based maintenance optimization in offshore wind farms using long-term wind speed forecasting

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  • Zhang, Fengqi
  • Si, Guojin
  • Chen, Zhen
  • Zheng, Meimei
  • Xia, Tangbin
  • Xi, Lifeng

Abstract

Offshore wind energy holds great potential but faces economic challenges due to remote and harsh environments. To address uncertainties in maintenance decision-making, this paper presents a Multi-Faceted Opportunistic-Based Maintenance (MFOBM) strategy that integrates long-term wind speed forecasts with multi-faceted maintenance opportunities. First, the predictive algorithm combines mode decomposition and Sequence-to-Sequence (Seq2Seq) models to accurately forecast 24-h wind speeds, optimizing schedules with high precision. Then the maintenance approach leverages multiple opportunities, including wind speed (triggered by projected low production levels) and vessel deployment (triggered by the potential for group maintenance), while aligning tasks with accessibility windows to mitigate adverse marine conditions. A rolling-horizon mixed-integer linear programming (MILP) model iteratively schedules preventive maintenance (PM) and corrective maintenance (CM), allowing real-time adjustments for emerging maintenance demands. Applied to a representative 132 MW offshore wind farm on Long Island off the U.S. East Coast, results demonstrate its effectiveness in reducing production losses, dispatch costs, and downtime while ensuring accessibility and flexibility. The dynamic nature of MFOBM enables prompt responses to real-time needs, supported by accurate wind forecasts, enhancing practical applicability. The MFOBM strategy provides a robust decision-making tool for optimizing maintenance in fluctuating and uncertain offshore environments, addressing both cost-effectiveness and operational reliability.

Suggested Citation

  • Zhang, Fengqi & Si, Guojin & Chen, Zhen & Zheng, Meimei & Xia, Tangbin & Xi, Lifeng, 2025. "A multi-faceted opportunistic-based maintenance optimization in offshore wind farms using long-term wind speed forecasting," Renewable Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:renene:v:255:y:2025:i:c:s0960148125014909
    DOI: 10.1016/j.renene.2025.123826
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