An optimization method for maintenance routing and scheduling in offshore wind farms based on chaotic quantum Harris hawks optimization

Optimizing the operation and maintenance (O&M) schedule of offshore wind farms is an effective approach to controlling O&M costs. This paper takes the perspective of appropriately increasing O&M costs to enhance environmental protection around the wind farm and improve the safety of the O&M process. Firstly, the minimization of total O&M cost, carbon emissions from O&M vessels, and the standard deviation of vessel sailing duration are set as optimization objectives. Constraints are determined, including vessel flow conservation, O&M personnel and spare parts, O&M time, external factors, and decision variable types. A new O&M scheduling model for offshore wind farms, namely the CCB-O&MS model, is constructed. A new algorithm, called the chaotic quantum Harris hawks optimization (CQMHHO), is also proposed. Additionally, the CCB-O&MS model's encoding rules (CRD) are designed, a feasible integerization algorithm (FIA) is developed, and a method for determining the number of wind turbines to be accessed (M-DNTA) is formulated. Consequently, a new optimization method for offshore wind farm O&M scheduling, namely CQMHHO-CCB-O&MS, has been established. Subsequently, the feasibility and superiority of the proposed scheduling model is tested using data from offshore wind farms in southern China. Experiment results demonstrate that, compared to other alternative models, the proposed model can achieve a comprehensive optimal scheduling plan. The improved algorithm is more effective than other benchmark algorithms in solving the CCB-O&MS model.