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As global demand for offshore wind energy continues to rise, the imperative to enhance the profitability of wind power projects and reduce their operational costs becomes increasingly urgent. This study proposes an innovative approach to optimize the inspection routes of offshore wind farms, which integrates the K-means clustering algorithm and genetic algorithm (GA). In this paper, the inspection route planning problem is formulated as a multiple traveling salesman problem (mTSP), and the advantages of the K-means clustering algorithm in distance similarity are utilized to effectively group the positions of wind turbines, thereby optimizing the inspection schedule for vessels. Subsequently, by harnessing the powerful optimization capability and robustness of genetic algorithms, further refinement is conducted to search for the optimal inspection routes, aiming to achieve cost reduction objectives. The results of simulation experiments demonstrate the effectiveness of this integrated approach. Compared to traditional genetic algorithms, the inspection route length has been significantly reduced, from 93 kilometers to 79.36 kilometers. Simultaneously, operational costs have also experienced a notable decrease, dropping from 141,500 Chinese Yuan to 125,600 Chinese Yuan.

随着全球海上风电能源需求持续攀升，提升风电项目盈利能力、降低运维成本的需求愈发迫切。本研究提出一种融合K-means聚类算法（K-means Clustering Algorithm）与遗传算法（Genetic Algorithm, GA）的海上风电场巡检路线优化创新方案。本文将巡检路线规划问题建模为多旅行商问题（Multiple Traveling Salesman Problem, mTSP），并利用K-means聚类算法在距离相似度分析上的优势，对风力发电机组的机位进行有效分组，以此优化船舶巡检调度计划。随后，借助遗传算法强大的优化能力与鲁棒性，开展进一步精细化优化，搜索最优巡检路线，以达成降本目标。仿真实验结果验证了该集成方案的有效性：与传统遗传算法相比，巡检路线总长显著缩短，从93公里降至79.36公里；同时运维成本亦出现明显下降，从141500元人民币降至125600元人民币。