Stochastic optimization of predictive maintenance scheduling for offshore wind farms
M. Borsotti (TU Delft - Mechanical Engineering)
X. Jiang (TU Delft - Mechanical Engineering)
R. R. Negenborn (TU Delft - Mechanical Engineering)
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Abstract
This paper presents a stochastic optimization model for predictive maintenance scheduling in offshore wind farms. The proposed model integrates probabilistic Remaining Useful Life (RUL) prognosis with mathematical optimization and Model Predictive Control (MPC) techniques that updates RUL beliefs with new prognostic measurements at each epoch to dynamically adjust maintenance decisions. Unlike conventional scheduling methods that rely on static age thresholds, our approach uses real-time prognostics to improve cost efficiency and reduce downtime. A case study on 50 wind turbines demonstrates that dynamically adapting maintenance schedules using prognostics reduces O&M expenses by 8.7%, primarily through significant reductions in downtime, compared to traditional methods.
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