A novel two-dimensional entrainment wake model for wind turbine wakes

Abstract

ABSTRACT The wake model for stand-alone turbine is an essential ingredient of wind farm wake research, which is of great concern for optimization of wind turbine layout, power generation simulation, and operation control on a wind farm. One-dimensional wake models (e.g., Jensen model and Frandsen model) based on single conservation theorem were approximately described by top-hat shape, which cannot represent the non-uniform distribution characteristics of wind turbine wake in the crosswind direction accurately and tend to underestimate the velocity deficit. Different from the above-mentioned models, entrainment wake model (EWM) can conserve both of mass and momentum within the control volume, which improves the accuracy of wake simulation to a certain extent. But the top-hat hypothesis limits the wake prediction accuracy of EWM. In this case, a novel two-dimensional EWM is proposed for the first time in this paper. Different from the advanced two-dimensional Bastankah and Porté-Agel wake model and Jensen-Gaussian wake model, the proposed model considers the influence of variable entrainment parameter on wake recovery, of which the key entrainment parameter was considered as a variable that is associated with downstream distance, ambient turbulence, and wake turbulence. The proposed Gaussian EWM has been validated by the data from Garrad Hassan (GH) wind tunnel laboratory experiments, Sexberium wind farm measurements, and the field measurements with lidar in Northwestern China. It has been concluded that the proposed model can predict the wake velocity distribution most accurately and provides the minimal power prediction error under various scenarios.