Synergistic operation and maintenance enabling lifecycle-aware opportunistic management of offshore wind energy
Jiaxin Zhang, You Dong, Dan M. Frangopol, Songye Zhu, Hongxing Yang
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.