Genetic optimisation for a stochastic model for opportunistic maintenance planning of offshore wind farms

The cost incurred from operations and maintenance activity for offshore wind turbines contributes significantly to the overall lifecycle cost for offshore wind farms. This work addresses the issue of identifying a near-optimal schedule for the planning of operations and maintenance activity for the turbines in an offshore wind farm. Significant cost savings can be realized by scheduling maintenance tasks at times of predicted low power production. This research implements a rolling horizon stochastic model and applies meta-heuristic optimization techniques to identify a near-optimal schedule for the opportunistic maintenance of wind farms. Empirical evaluation of the proposed approach produces schedules that achieve a cost saving in the range of 13–21% over standard techniques.