As the offshore wind industry expands, improving operation and maintenance (O & M) efficiency while reducing the levelized cost of electricity (LCOE) has become increasingly important. This study develops an intelligent inspection route optimization framework for 21 offshore wind turbines located in the Changhua offshore wind farm of Taiwan. The framework integrates Geographic Information System (GIS) spatial information, dynamic sea-state conditions, labor costs based on Taiwan’s Labor Standards Act, and vessel fuel consumption into a comprehensive cost evaluation model. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to two practical scenarios: full-field routine inspections and targeted maintenance missions. Experimental results show that PSO achieved the shortest route for full-field inspections, reducing the travel distance to 20.125 km compared with 23.976 km obtained by GA. In contrast, for targeted maintenance involving eight turbines, GA generated a shorter route of 5.719 km, outperforming PSO’s 6.456 km. For the scenarios investigated in this study, PSO showed superior performance in the 21-turbine inspection task, whereas GA achieved better results in the 8-turbine maintenance task. The proposed framework provides an effective decision-support tool for offshore wind farm O & M planning, improving maintenance efficiency while reducing operational costs.
Wang et al. (Mon,) studied this question.