Data driven models for understanding the wind farm wake propagation pattern

Abstract

In a wind farm, where several wind turbines are arranged in rows and columns, the wind speed available for the downstream turbines are significantly reduced by the wake effect. The wake losses can reduce the total productivity of a wind farm up to 20 per cent. Understanding the wake pattern in an existing wind farm is essential for the short-term wind power forecast. In this paper, we propose the use of artificial intelligence to understand the wind flow pattern and thereby the wake induced power losses within an existing wind farm. The farm considered for this study has 64 wind turbines of 2 MW rated capacity. Three learning methods based on artificial intelligence are used for the study. These are (i) Artificial Neural Network (ANN), (ii) Support Vector Machines (SVM), and (iii) K-Nearest Neighbors (KNN). The accuracies of these models, based on the error between the estimated and observed power produced by the turbines, are also presented.