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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.