Review of deep reinforcement learning for offshore wind farm maintenance planning

Abstract

Offshore wind farms face unique challenges in maintenance due to harsh weather, remote locations, and complex logistics. Traditional maintenance strategies often fail to optimize operations, leading to unplanned failures or unnecessary servicing. In recent years, deep reinforcement learning (DRL) has shown clear potential to tackle these challenges through a data-driven approach. This paper provides a critical review of representative DRL models for offshore wind farm maintenance planning, elaborating on both single- and multi-agent frameworks, diverse training algorithms, various problem formulations, and the integration of domain-specific knowledge. The review compares the benefits and limitations of these methods, identifying a significant gap in the widely adopted use of simplistic binary maintenance decisions, rather than including multi-level or imperfect repairs in the action space. In conclusion, this work suggests directions for future research to overcome current limitations and enhance the applicability of DRL methods in offshore wind maintenance.