Towards Learning-Based Decision-Making for Maintenance of Offshore Wind Farms
M. Borsotti (TU Delft - Mechanical Engineering)
R.R. Negenborn – Promotor (TU Delft - Mechanical Engineering)
X. Jiang – Copromotor (TU Delft - Mechanical Engineering)
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Abstract
Offshore wind maintenance requires decisions under uncertain component health, weather-driven accessibility, logistical constraints, and costly downtime. This thesis develops a framework to investigate how prognostic information can
support such decisions. It compares optimization-based maintenance planning using mixed-integer linear programming with learning-based planning using deep reinforcement learning. The results highlight their respective strengths and
limitations, supporting the development of more adaptive, reliable, and cost-effective offshore wind O&M decision-support systems.