Modeling the equivalent of doubly fed wind turbine clusters using the K-means algorithm

Due to the swift growth in renewable energy sources, wind energy now constitutes a significant component of the worldwide energy framework. Incorporating wind farms into the existing power grid introduces novel difficulties concerning the grid's stability and dependability. In this paper, Wind velocity serves as the key parameter for categorizing wind patterns, combined with the pitch angle control, the K-means clustering algorithm is used to divide the wind field into three categories, and then the three types of wind fields are modeled with equivalent values, and finally the parameters of the equivalent wind turbines are identified, the discrepancy between the outcomes derived and the actual measured data is minimal, indicating that the equivalent model can better simulate the operation state of the actual wind farm. As the scale of new energy sources continues to grow, wind turbines have become increasingly important within the power grid.