Lifecycle-aware intelligent operation and maintenance for floating offshore wind energy

Abstract

Floating Offshore Wind Turbines (FOWTs) offer a promising solution for accessing deep-sea wind resources, but ensuring 25-year structural reliability under corrosive marine conditions, coupled wind-wave loading, and constrained offshore accessibility remains a significant challenge. High-fidelity aero-hydro-servo-elastic simulators are computationally prohibitive for full life-cycle analysis. Traditional operation and maintenance (O&M) strategies often decouple control actions from offshore logistics, leading to either excessive downtime or deferred interventions. Moreover, current studies seldom bridge long-term structural degradation with real-time control or farm-level aerodynamic interactions. This study aims to elucidate the degradation mechanisms across four critical subsystems, blade, generator, tower, and moorings, under coupled environmental and operational loads; to develop fast yet accurate predictive models; and to establish an opportunistic operation and maintenance (OppOM) strategy that integrates control decisions with offshore maintenance scheduling, from the scale of a single turbine to an entire wind farm. A high-fidelity OpenFAST simulation framework is first developed to capture the coupled dynamic response of all four subsystems under various wind, wave, and control conditions. The analysis reveals that the generator and tower are highly sensitive to control parameters near rated wind speed, whereas mooring loads are predominantly driven by wave conditions. Moderate de-rating is shown to effectively compress the fatigue stress envelope across components. Building upon this, a probabilistic corrosion-fatigue framework is established to evaluate the time-varying reliability of each subsystem, combining site-specific environmental data with dynamic stress histories. The results identify the tower shell and generator components as critical to early-life degradation, and demonstrate that control tuning can significantly postpone reliability decline with minimal impact on energy production. To address the computational limitations of real-time assessment, a physics-informed machine learning (PIML) surrogate model is developed. By embedding resonance-aware constraints into the learning process, the model accurately reproduces structural responses at second-level speed, thus enabling fast prediction of dynamic states for digital-twin integration. The surrogate outputs are then fed into a hybrid decision-making framework, where a Dynamic Bayesian Network (DBN) describes subsystem degradation, a Partially Observable Markov Decision Process (POMDP) models uncertainty, and an A3C reinforcement learning agent jointly optimizes control, de-rating, and maintenance actions. Compared with traditional condition-based and opportunistic strategies, this OppOM framework extends offshore maintenance intervals while simultaneously reducing lifetime risk and operational costs. Finally, the method is scaled to a three-turbine wind farm, incorporating dynamic wake interactions and future climate scenarios. Coordinated de-rating of the upstream turbine mitigates wake effects on downstream units, improves overall energy yield and logistical efficiency, and maintains high reliability even under severe climate-change projections. By integrating high-fidelity simulation, physics-enhanced surrogate modeling, and reinforcement-learning-based decision-making into a digital-twin framework, this work offers a scalable and intelligent solution for the reliable, economical, and low-carbon operation of deep-sea floating wind turbines. The methodology also lays the groundwork for future advancements in cloud-edge digital twins, large language model integration, and multi-farm, grid-interactive optimization.