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

• Published in: Ocean engineering 2024-09, Vol.308, p.118306, Article 118306
• Main Authors: Li, Ming-Wei, Lei, Yi-Zhang, Yang, Zhong-Yi, Huang, Hsin-Pou, Hong, Wei-Chiang
• Format: Article
• Language: English
• Subjects: Chaotic mapping; Harris hawks optimization; Offshore wind farm; Operation and maintenance scheduling; Quantum computing
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.118306

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. •A new chaotic quantum Harris hawk optimization named CQMHHO is proposed to address the issues of premature convergence.•The proposed algorithm introduces a fast search strategy based on quantum computing.•Experimental results demonstrate that CQMHHO outperforms other benchmarked algorithms when applied to solve CCB-O&MS.•A novel offshore wind farm operation and maintenance scheduling method, namely CQMHHO-CCB-O&MS, is established.•The proposed CQMHHO-CCB-O&MS model can reduce the carbon emissions of vessels and balance the duration of vessel sailing.