Genetic programming hyper-heuristic for evolving a maintenance policy for wind farms

Reducing the cost of operating and maintaining wind farms is essential for the economic viability of this renewable energy source. This study applies hyper-heuristics to design a maintenance policy that prescribes the best maintenance action in every possible situation. Genetic programming is used to construct a priority function that determines what maintenance activities to conduct and the sequence of maintenance activities if there are not enough resources to do all of them simultaneously. The priority function may take into account the health condition of the target turbine and its components, the characteristics of the corresponding maintenance work, the workload of the maintenance crew, the working condition of the whole wind farm and the possibilities provided by opportunistic maintenance. Empirical results using a simulation model of the wind farm demonstrate that the proposed model can construct maintenance policies that perform well both in training and test scenarios, which shows the practicability of the approach.