Joint optimization of spare parts inventory and maintenance for wind turbine systems

Abstract

This paper focuses on optimizing maintenance and spare parts inventory strategies for wind turbines, specifically targeting a four-component system with series-parallel structural. We explore the joint decision-making and optimization problems of maintenance and spare parts inventory by modeling the deterioration process of the system using stochastic processes. Moreover, we formulate corresponding maintenance plans and spare parts ordering strategies for components with different degrees of importance by considering the structural and stochastic correlations among components, as well as the delay in spare parts supply. To address the challenges of joint optimization, we develop a hybrid algorithm that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), termed the GA-PSO algorithm, to solve the strategies of the constructed model. Finally, it is verified that the effectiveness, high performance and robustness, of the proposed strategies, algorithms, through numerical case analysis, comparative experiments as well as analysis regarding the impact of key parameters on the model. Sensitivity analysis reveals that random correlation between key and non-key components has relatively high sensitivity on the model, which should be paid more attention.