Multi-objective optimization of preventive maintenance strategies for floating wind farms considering economic efficiency, system reliability, and power generation

Floating offshore wind power has become an important contributor to the transition toward low-carbon energy systems, increasing the need for effective operation and maintenance (O&M) strategies. This study develops a preventive maintenance framework based on a degradation-failure-maintenance model, in which component degradation is described by Weibull distributions and multiple maintenance actions, including preventive maintenance, major repair, and corrective replacement, are incorporated. Maintenance cost, system reliability, and power generation efficiency are formulated as conflicting objectives and optimized using the non-dominated sorting genetic algorithm II (NSGA-II). A case study is conducted to evaluate the proposed framework and examine the influence of four key decision variables on maintenance performance. The results demonstrate that optimized maintenance strategy reduces costs, improves reliability, and minimizes energy loss. Sensitivity analysis evaluates the influence of critical parameters on outputs. The proposed methodology provides practical decision support for efficient O&M planning of floating offshore wind farms.