Synergistic operation and maintenance enabling lifecycle-aware opportunistic management of offshore wind energy

Abstract

Offshore wind power capitalizes on abundant wind resources and vast spatial availability, enabling a significant increase in turbine capacity. However, the deterioration of large-scale floating offshore wind turbines (FOWTs) under complex marine conditions remains a persistent challenge. Rapid structural degradation and the inaccessibility of far-offshore wind farms pose substantial hurdles to effective operation and maintenance (O&M) strategies. To address these challenges, an opportunistic operation and maintenance (OppOM) framework is proposed, integrating turbine de-rating control with maintenance scheduling to enable intelligent management over the lifecycle. The system state evolution of FOWTs under dynamic wind–wave environment is inferred using a Dynamic Bayesian Network (DBN). A Partially Observable Markov Decision Process (POMDP) then models the uncertainty in observations and guides decision-making through probabilistic reasoning. A multi-attribute utility function is developed to jointly consider turbine health, economic costs, energy yield, and carbon emissions as lifecycle O&M objectives. The integrated DBN-POMDP framework is ultimately solved using an Asynchronous Advantage Actor-Critic reinforcement learning approach. The proposed OppOM framework was benchmarked against conventional Condition-base maintenance (CBM) and de-rating free opportunistic maintenance (OppM). Compared to CBM, OppOM reduced total lifecycle costs by 30.4%. Relative to OppM, it achieved an 18.7% cost reduction, 12.7% less downtime, and notable gains in energy output and CO₂ mitigation. Average system health index increased to 0.87, while component-level HI remained above 0.95 across the service life. The proposed OppOM framework establishes a new paradigm for offshore wind energy O&M by unifying structural control and maintenance planning. By incorporating turbine self-adaptive behavior into long-term governance, it enhances resilience to environmental uncertainty while improving lifecycle-level sustainability. • Develop an opportunistic operation and maintenance framework for offshore energy. • Integrate structural control and maintenance scheduling under coupled wind-wave. • Establish a DBN–POMDP model for lifecycle decision-making in dynamic environments. • Propose a multi-attribute utility function covering cost, reliability, and sustainability. • Apply deep reinforcement learning to optimize life-cycle O&M strategies.