Objective As China's offshore wind energy rapidly expands into far-offshore and deep-sea areas, severe sea conditions (wave heights: 2.5–3.5 m; wind speeds: 15–25 m/s) have become a critical bottleneck for operational and maintenance (O&M) operations. Conventional wave compensation trestles suffer from low wave prediction accuracy, delayed response, and inadequate control strategies, leading to insufficient mitigation of relative motion between vessels and wind turbine platforms, high safety risks for personnel transfer, and extremely low sea state window utilization (only 8.0% under high sea conditions). To address these challenges, this study proposes an integrated solution combining a Fractal Reinforcement Learning (FRL) adaptive algorithm with a six-degree-of-freedom (6-DOF) hydraulic trestle system, aiming to enhance O&M safety, efficiency, and economy.

Methods Focusing on a deep-sea wind farm off Guangdong Province, the research leverages three years of measured sea state data (2021–2023) to develop the FRL adaptive compensation algorithm, which synthesizes wave fractal self-similarity with the Deep Deterministic Policy Gradient (DDPG) continuous control algorithm. A 6-DOF hydraulic trestle system was designed, integrating feedforward-feedback dual-mode control for rapid disturbance rejection and multi-layered safety mechanisms (overload protection, limit protection, and emergency braking). Six months of full-scale field trials (covering typhoon and winter seasons) were conducted, combined with ablation experiments and statistical significance tests (t-tests and ANOVA) to verify system performance. A comprehensive economic analysis based on a 5-year O&M cycle was also performed.

Results Under severe sea conditions, the system maintained stable compensation errors of 0.45±0.08 m (below 0.5 m) with response times of 0.08–0.09 seconds, outperforming conventional systems by 64% in precision and 64% in response speed. No personnel transfer accidents occurred during the trials. The sea state window utilization rate increased to 81.0% (a 9.1-fold improvement), and personnel transfer efficiency reached 31 individuals per day (a 3.4-fold uplift). Economic assessment showed a five-year static Return on Investment (ROI) of 338.1% and a payback period of 0.98 years. T-tests on ablation results confirmed that the fractal interpolation module (improving wave prediction accuracy by 25–30%) and reinforcement learning component (reducing control errors by 61.5%) significantly enhanced compensation precision (p<0.05 and p<0.01, respectively). The "hierarchical transfer + online fine-tuning" strategy minimized simulation-to-reality performance loss to less than 10%.

Conclusions The proposed framework achieves deep integration of algorithmic accuracy, system reliability, and economic viability, forming a cohesive "algorithm-system-verification-benefit" architecture. Validated through six months of offshore field trials and five years of quantitative analysis, it provides robust technical support for far-offshore wind power O&M. The architecture and methodology offer a standardized reference for complex marine engineering applications such as adaptive control of marine equipment, facilitating the intelligent and economical upgrading of offshore engineering equipment.