LAUSR

With the increasing scale of wind farms, the fault characteristics tend to be complex, which poses a technical challenge to establish the dynamic equivalent model of wind farms. In this paper, a dynamic equivalent method of DFIG-based wind farm based on the density peak clustering algorithm (DPCA) is presented. First, under an analysis of short-circuit current (SCC) in single doubly fed induction generator (DFIG), the clustering indexes are selected. Second, with the selected clustering indexes and DPCA, a more refined two-stage clustering of DFIGs in wind farm is carried out. Third, the units in the same cluster are equivalent to one unit, and then the dynamic equivalent model of the DFIG-based wind farm is established. Finally, the proposed method is validated through the MATLAB/SIMULINK-based simulation results, and the comparison results also show that the dynamic equivalent model proposed in this paper has a better performance than two other equivalent models. Moreover, another comparison between DPCA and K-means clustering algorithm is analyzed, and the result shows that DPCA has a better performance which provides a better choice for dynamic equivalence of wind farms.