An advanced analytical model for wind-turbine wakes based on an elliptic super-Gaussian function and adaptive parameters selection strategy

Accurate prediction of wind turbine wakes is essential for optimizing wind farm layouts. This study proposes a novel analytical wake model (3DAESG) that integrates an elliptic super-Gaussian function with an adaptive parameter selection strategy using a genetic algorithm. Unlike conventional Gaussian-based models, the 3DAESG model captures the transition from a top-hat profile in the near-wake region to a Gaussian shape in the far-wake area. Validated against four distinct datasets—field measurements, wind tunnel tests, and large-eddy simulation data—the model achieves a maximum prediction error of less than 10%. Comparative evaluations demonstrate its improved predictive accuracy, especially in the near-wake zone and around the turbine hub height. Its computational efficiency makes it well-suited for practical wind farm planning and operation applications.