Distributed Model-Free Turbine Repositioning for Wake Overlap Minimization in Floating Offshore Wind Farms
Abstract
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.
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BibTeX
@misc{chen2026distributed,
title = {{Distributed Model-Free Turbine Repositioning for Wake Overlap Minimization in Floating Offshore Wind Farms}},
author = {Zekai Chen and Ryozo Nagamune},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08646},
eprint = {2610.08646},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08646},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Distributed Model-Free Turbine Repositioning for Wake Overlap Minimization in Floating Offshore Wind Farms
AU - Zekai Chen
AU - Ryozo Nagamune
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.08646
UR - https://arxiv.org/abs/2610.08646
AB - 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.
ER -