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The reliability analysis of offshore wind turbines is increasingly significant due to the system complexity and negative impacts in harsh operating conditions. Maintenance processes are required to ensure the system safety and performance. In this paper, an opportunistic maintenance strategy for an offshore wind turbine electrical and electronic system is proposed, considering imperfect maintenance and preventive maintenance durations. When one component reaches its reliability threshold, opportunistic preventive maintenance can be implemented for others. Based on the rolling horizon approach, the maintenance planning can be updated to take short-term information into account, which could be changed with time. By selecting the best combination, the maintenance schedule during the mission time is finally determined. Failure information is collected from previous literature studies to accomplish the calculations. The results show that the maintenance cost is significantly reduced by applying opportunistic maintenance and the control is the largest contributor to overall cost savings, followed by sensors.

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Offshore wind is considered a crucial part in the future energy supply. However, influenced by weather conditions, the maintenance of offshore wind turbine system (OWTs) equipment is challenged by poor accessibility and serious failure consequences. It is necessary to study the optimized strategy of comprehensive maintenance for offshore wind farms, with consideration of the influences of incomplete equipment maintenance, weather accessibility and economic relevance. In this paper, a Monte Carlo algorithm-improved factor is presented to simulate the imperfect preventive maintenance activity, and waiting windows were created to study the accessibility of weather conditions. Based on a rolling horizon approach, an opportunity group maintenance model of an offshore wind farm was proposed. The maintenance correlations between systems and between equipment as well as breakdown losses, maintenance uncertainty, and weather conditions were taken into account in the model, thus realizing coordination of maintenance activities of different systems and different equipment. The proposed model was applied to calculate the maintenance cost of the Dafengtian Offshore Wind Farm in China. Results proved that the proposed model could realize long-term dynamic optimization of offshore wind farm maintenance activities, increase the total availability of the wind power system and reduce total maintenance costs.