Dynamic equivalence method considering the spatial effect of wind farms

A dynamic equivalent model suitable for wind farms based on clustering algorithms and experimental data is presented in this paper. According to the experimental data of one wind farm, Existence of dispersion between wind farm units is proved by the way of comparison of random sampling. Besides, the study making use of the significant difference between wind speed curves and power curves in different units indicates that the dispersion among wind farm units can't be ignored. In the new model, the wind farms' 33 UP77-1.5MW wind turbine units are clustered into four categories by making use of the K-means cluster analysis of SPSS platform and using the measured data as clustering index. At last, the macro comparison and error analysis between various models and measured data verifies the rationality of the model. In addition, the result of comparing with the traditional model indicates that the model in the new method of this paper has a higher accuracy.