ANALYSIS OF WAKE EFFECTS AND LAYOUT OPTIMIZATION METHODS FOR OFFSHORE WIND FARMS

This paper addresses the issues of energy loss and equipment fatigue caused by wake effects in offshore wind farms by proposing a hybrid prediction model that integrates physical mechanisms with data-driven approaches, along with a layout optimization framework based on a multi-objective evolutionary algorithm. First, through improved RANS numerical simulations, scaled wind tunnel experiments, and high-resolution LES local simulations, the study systematically reveals the coupling effects of different planar layouts, wind speed conditions, and turbine height differences on wake velocity attenuation, turbulence regeneration, and vortex structure evolution. Second, a hybrid prediction model integrating analytical model priors with corrections from XGBoost and residual network compensation is constructed, enabling high-precision, low-latency online prediction of wake decay rates and turbulence intensity under unknown layouts and extreme wind conditions. Finally, by incorporating annualized power generation, fatigue load fluctuations of critical components, and operational costs into a unified multi-objective function, Pareto front search is employed to generate multiple sets of optimal layout schemes. The robustness of these schemes is validated through sensitivity and uncertainty analyses. Case studies demonstrate that optimized wind farms can increase annual power generation by 3%–7%, reduce load fluctuations by 10%–15%, and lower operational costs by 5%–9%, while simultaneously decreasing maintenance vessel dispatch frequency, reducing marine noise pollution, and significantly enhancing carbon reduction benefits. The research outcomes not only enrich wake dynamics and turbulence regeneration theories but also provide practical, visualized decision-support tools for the design, construction, and operation of offshore wind farms, holding significant importance for advancing the intelligent and low-cost development of the wind power industry.