In view of inefficient analysis process for wind turbine performance

inaccurate data cleaning and hard to identifying of wind power generation status

a data cleaning algorithm is put forward to analyze the wind turbines power curve. Through the analysis of the wind power curve data collected by supervisory control and data acquisition(SCADA)system

the optimal interclass variance algorithm is proposed to identify the poor performance status accurately with optimizing data processing rules and analysis approach. The detection tools and data dimension are not the obstacles of power curve analysis. Example analysis shows that the method is practical and efficient

and can accurately clean wind turbines power curve data and identify abnormal status performance under the premise of no increase in the hardware equipment investment

while significantly improving the wind turbines performance analysis accuracy.This work is supported by Jilin Province Science and Technology Development Project(No. 20150204084GX).