发表机构
the University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种分布式无模型优化方法，利用物理知识实现浮式海上风电场涡轮机重定位，以最小化尾流重叠，相比集中式基线性能略优且计算成本显著降低。
AI 中文摘要
本文提出了一种用于浮式海上风电场中涡轮机重定位控制的无模型方法。该方法采用分布式在线优化框架来减轻尾流效应。传统方法依赖于集中式、基于模型的控制架构，这些架构在实际可行性方面表现不佳。为克服这一局限并应对非凸性和不可获取的梯度信息问题，我们将尾流动力的物理知识融入控制系统设计中。我们通过在中保真度模拟中与集中式、基于模型的基线进行比较，证明了所提方法的有效性。结果表明，所提方法在性能上略优，同时显著降低了计算成本。
英文摘要
This paper presents a model-free method for turbine repositioning control in floating offshore wind farms. The method uses a distributed online optimization framework to mitigate the wake effect. Conventional approaches rely on centralized, model-based control architectures, which suffer from poor practical feasibility. To overcome this limitation and address nonconvexity and inaccessible gradient information, we incorporate physics-informed knowledge of wake dynamics into the control system design. We demonstrate the effectiveness of the proposed method by comparing it with a centralized, model-based baseline in mid-fidelity simulations. Results indicate that the proposed method achieves slightly better performance while significantly reducing computational cost.
CommentsThis paper has been submitted to the American Control Conference (ACC) 2027
