A closed-loop maintenance strategy for offshore wind farms: Incorporating dynamic wind farm states and uncertainty-awareness in decision-making

The determination of maintenance strategies is subject to complexity and uncertainty arising from variable offshore wind farm states and inaccuracies in model parameters. The most common method in the existing studies is to adopt an open-loop approach to optimize a maintenance strategy. However, this approach lacks the ability to capture periodic operational state of the wind farm and the awareness of eliminating uncertainty. Consequently, the determined strategy is inadequate to instruct maintenance activities, inducing excessive revenue losses. In this paper, a closed-loop maintenance strategy optimization method is proposed for decision-makers to identify a more profitable manner of wind farm maintenance management. The life-cycle maintenance optimization problem is decomposed into a sequence of sub-optimization problems covering multiple time periods by using a rolling-horizon approach. Each sub-optimization problem is intentionally designed based on the monitored state of the wind farm and the available reliability, availability, and maintainability (RAM) database. Meanwhile, the decision maker consciously mitigates the parameter uncertainty in the maintenance model gradually by updating the current database. Compared to conventional strategies covering the entire lifetime of wind farms, the proposed maintenance strategy is periodically adjusted to provide a series of sub-strategies. The proposed approach was applied in a simulation experiment, a generic small-scale offshore wind farm, to assess its performance. Computational results show that adapting maintenance strategies based on the current state of the wind farm can reduce revenue losses in comparison to conventional open-loop strategies. In addition, the benefits of updating the RAM database in decreasing revenue losses is revealed.