Improved Wind Farm Aggregated Modeling Method for Large-Scale Power System Stability Studies

Nowadays, with the highly penetrated wind generations, the accurate wind farm (WF) model is required for power system stability analysis. Due to the complexity on detailed WF model, the aggregated model, with a reasonable reduction of the detailed model meanwhile retaining the required level of accuracy is essential to be developed. In this paper, an improved WF aggregated modeling method for large-scale power system stability studies is proposed. To overcome the limitations of the traditional methods, a geometric template matching based time series wind turbines clustering method is developed. Moreover, a multiobjective optimization algorithm, which fully considered the wind speed disturbance and system fault together, is designed to identify both the generator and control parameters. Additionally, to shrink the size of the identified parameters and increase the modeling accuracy, a sensitivity and correlation analysis based key parameters selection scheme is also adopted. To verify the effectiveness of the proposed method, dynamic responses of the proposed aggregated model are compared against the responses of the traditional equivalent model for various wind scenarios through an actual case.