Data supporting the research: Towards Learning-Based Decision-Making for Maintenance of Offshore Wind Farms
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Offshore wind farms are central to the energy transition, yet their long-term competitiveness remains strongly affected by the cost and complexity of operations and maintenance. Maintenance decisions must be made under uncertain component degradation, weather-constrained accessibility, scarce marine logistics resources, and high downtime costs. This thesis develops intelligent decision-support methods for offshore wind farm maintenance, focusing on the trade-offs between optimisation-based and learning-based planning approaches.First, the thesis reviews the state of the art in offshore wind O&M decision support across strategic, tactical, and operational decision-making levels, identifying persistent gaps in the integration of metocean accessibility, logistics, and condition-informed planning. A modelling environment is then developed to enable controlled comparison between decision-making methods. This environment includes the configuration of a fixed-bottom offshore wind farm, critical turbine components, vessel-specific accessibility constraints, metocean data, probabilistic remaining useful life predictions, maintenance actions, and cost and downtime models.Building on this …
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