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The paper proposes an innovative probabilistic clustering concept for aggregate modeling of wind farms (WFs). The proposed technique determines the number of equivalent turbines that can be used to represent large WF during the year in system studies. Support vector clustering (SVC) technique is used to cluster wind turbines (WTs) based on WF layout and incoming wind. These clusters are then arranged into groups, and finally through analysis of wind at the site, equivalent number of WTs for WF representation is determined. The method is demonstrated on a WF consisting of 49 WTs connected to the grid through two transmission lines. Dynamic responses of the aggregate model of the WF are compared against responses of the full WF model for various wind scenarios. [ABSTRACT FROM PUBLISHER]

This paper presents an original and effective application of support vector clustering (SVC) to electrical load pattern classification. The proposed SVC-based approach combines the calculation of the support vectors, carried out by using a classical procedure adopting a Gaussian kernel, with a specifically developed deterministic algorithm to form the clusters. This algorithm exploits the meaningful location of the bounded support vectors (BSVs) to define the outliers, identifying the clusters in function of the distance of the non-BSVs to the BSVs. Its implementation is less computationally intensive than other existing approaches and the cluster formation is driven by a single user-defined threshold. Extended comparison to other clustering methods is included to show the effectiveness of the proposed approach in grouping multidimensional load pattern data into non-overlapping clusters. This effectiveness is confirmed by the calculation of various cluster validity indicators. In particular, the most successful tasks are the identification of the outliers and the more effective formation of small numbers of clusters with respect to other methods. [ABSTRACT FROM AUTHOR]