用于海上风电场维护调度的深度强化学习集成方法

Abstract Offshore wind energy , a cornerstone of sustainable power generation , faces escalating operational challenges as farms expand to harness cost efficiencies, including the imperative to counteract power fluctuations caused by wake effects and weather volatility. This study introduces a domain-informed Deep Q-Network (DQN) framework, engineered to optimize the allocation of maintenance resources and the strategic selection of maintenance tasks, resulting in an 11.1% increase in power generation compared to default wind conditions. By incorporating multiple wake model for enhanced decision-making accuracy, the scheduling dilemma is formulated as Markov Decision Processes (MDPs) to navigate the complexities of maintenance scheduling. A notable innovation is the integration of convolutional layers , which expedite algorithmic convergence. These results underscore the significant potential of our model to improve operational productivity in large-scale offshore wind farms .