The ideal wind turbine power curve provided by the manufacturer cannot monitor the practical performance of wind turbines accurately in the engineering stage; in this paper, a modified approach of the wind turbine power curve is proposed based on improved Bins and K-means++ clustering. By analyzing the wind speed-power data collected by the supervisory control and data acquisition system (SCADA), the relationship between wind speed and output is compared and elaborated on. On the basis of data preprocessing, an improved Bins method for equal frequency division of data is proposed, and the results are clustered through K-means++. Then, the wind turbine power curve correction is realized by data weighting and regression analysis. Finally, an example is given to show that the power curve of the same type of wind turbines, which, installed in different locations, are discrepant and different from the MPC, and the wind turbine power curve obtained by using this method can reflect the output characteristics of the wind turbine operating more effectively in a complex environment.
Keywords: Bins method; K-means++ clustering; manufacturer’s ideal power curve (MPC); practical power curve (PPC); wind turbine.