A novel entrainment wind farm flow model for power prediction

Abstract

ABSTRACT The wind farm flow (WFF) models, which are enabled to predict the power output of downstream, located turbines within a wind farm. A WFF model consists of two main model components: a single wake model and a superposition model. Two WFF models, the Root Sum Square (RSS) superposition model incorporated into the single wake models (e.g. Bastankah and Porté-Agel wake (BPA) model and Jensen wake model), have been extensively applied in engineering. But the WFF models above-mentioned tend to overestimate the power deficit of the whole wind farm. Hence, a novel entrainment wind farm flow (NEWFF) model is proposed in this paper, which is a combination of a modified linear entrainment wake (MLEW) model and the RSS superposition model. Different from the previous linear entrainment wake (LEW) model, the MLEW model developed in this paper considers the impacts of terrain roughness variables on the wake distribution of downstream wind turbines. The MLEW model significantly improves the accuracy of the wake simulation over the advanced BPAW model and JW model, as well as the LEW model, as shown in two cases from the TNO wind tunnel. Finally, several cases of Horns Rev offshore wind farm and Lillgrund offshore wind farm are utilized to validate the NEWFF model. Compared with the latest advanced Zong and Agel superposition wake (ZASW) model and BPA wind farm flow (BPAWFF) model, it has been demonstrated that the prediction results obtained with the NEWFF model exhibit the best agreements with measured power data under full and partial wake conditions.