Clustering-interpolation method and its application to wind turbine generator curve

Abstract

The real-time operating wind turbine power curve (WPC) of a wind turbine generator (WTG) is not completely identical to a WPC provided by the manufacturer because of various factors. In order to obtain an accurate WPC model that can consider various factors, this paper improves a bisecting k-means clustering algorithm. The improved clustering algorithm is used for partitioning the measured data into a certain number of groups, which can be expressed in their centroids. The interpolation method based on the polynomial is carried out for modelling a WPC of WTG. The modelled WPC is applied to the reliability analysis of the generating systems with a wind farm. The results show that the accuracy of the linear interpolation is higher than that of quadratic interpolation and cubic spline interpolation when there are a relatively large number of clusters.