Optimization Maintenance Scheduling for Offshore Wind Farms Based on an Improved Particle Swarm Optimization

In the context of the global shift towards sustainable energy sources, the critical significance of exploiting wind energy via offshore wind farms is progressively acknowledged. The chal-lenging marine conditions elevate the likelihood of wind turbine malfunctions, making the development of sound maintenance strategies critical for reducing operational costs and enhancing the efficiency of offshore wind energy production. In response to the urgent need for efficient maintenance strategies for offshore wind farms, this paper presents an Improved Particle Swarm Optimization (IPSO) algorithm. This novel algorithm introduces a dynamic retention probability adjustment to effectively bal-ance the exploratory and exploitative phases of the algorithm throughout iterations, promoting better convergence rates and solution quality. Key improvements include subsequence ex-changes to simulate velocity updates, which enhance diversity, and mutation operations that enable the algorithm to overcome local optima by introducing variability into the population. An adaptive acceptance criterion also allows for the selection of less optimal solutions under certain conditions, preventing premature convergence and strengthening the global search capability. These enhancements enable the IPSO algorithm to tackle complex optimization problems more effectively, particularly by ensuring diversity in solution space and avoiding local optima. The proposed maintenance strategies have been shown to notably decrease costs, enhancing the economic viability of offshore wind energy operations.