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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]

Abstract: The modelling of wind turbines with doubly-fed induction generator (DFIG) requires consideration of overall aerodynamic, mechanic, electromagnetic and control aspects, even in the case of DFIG representation in steady-state conditions for energy production assessment. This paper firstly summarizes the background concepts for interpreting the characteristic curves of the DFIG. Then, it considers and illustrates the structure and use of a dedicated equivalent circuit based on the incorporation of an apparent resistance in the model. Furthermore, a new method for correcting the experimental data gathered from wind turbines in practical applications is proposed, in order to make these data comparable with the quantities indicated by the manufacturers in the power-wind speed curve of the wind turbines. Application examples are provided by using data of real DFIG machines. [Copyright &y& Elsevier]