AI 中文总结
针对混合风浪能系统的复杂动力学问题，提出强化学习控制框架，基于高保真模拟训练，在波浪能捕获与平台运动抑制的帕累托优化上优于传统策略，大幅扩展系统性能边界。
AI 中文摘要
将波浪能转换器（WECs）与浮式海上风力涡轮机（FOWTs）集成，形成混合风浪能（HWWE）系统，是实现海上可再生能源进一步降本的有前景的方法。在此类系统中，集成WECs的控制发挥重要作用，有潜力在产生额外波浪能的同时抑制浮式平台运动。然而，HWWE系统具有复杂动力学特性，仅能通过数值模拟实现精准建模，给控制器设计带来重大挑战。本文提出一种针对HWWE系统的强化学习（RL）控制框架，其中实时控制策略通过与高保真模拟的交互直接学习。建立由IEA 15 MW风力涡轮机、VolturnUS半潜式平台和三个 torus 型WECs组成的HWWE系统数值模型，将其用作RL训练环境。从帕累托视角，在波浪能发电和平台运动抑制这两个相互冲突的目标下评估控制性能。结果表明，与传统控制策略相比，所提RL控制器实现了显著的帕累托改进：例如，在相同平台运动水平下，波浪能捕获量提高75%以上；或在相同能量捕获水平下，运动降低近50%，从而显著扩展了HWWE系统可实现的性能边界。
英文摘要
Integrating wave energy converters (WECs) with floating offshore wind turbines (FOWTs), to form hybrid wind-wave energy (HWWE) systems, is a promising approach to achieve further cost reduction for offshore renewable energy. In such systems, the control of the integrated WECs plays an important role, with the potential to generate additional wave energy while simultaneously suppressing floating platform motion. However, HWWE systems are characterized by complex dynamics, making accurate modelling only viable through numerical simulation, and posing significant challenges for control design. This paper proposes a reinforcement learning (RL) control framework for HWWE systems, in which the real-time control policy is learned directly through interactions with high-fidelity simulation. A numerical model is established for a HWWE system consisting of an IEA 15 MW wind turbine, a VolturnUS semi-submersible platform, and three torus-type WECs, which is then employed as the RL training environment. Control performance is evaluated in terms of both wave energy generation and platform motion reduction, two competing objectives, from a Pareto perspective. It is shown that the proposed RL controller achieves substantial Pareto improvements over conventional control strategies, e.g., over 75\% higher wave energy capture at the same platform motion level, or nearly 50\% lower motion at the same energy capture level, thereby significantly extending the attainable performance boundary of HWWE systems.
