A preprocessing algorithm for stator temperature based on wind power SCADA data has been put forward in view of deviant status of wind turbine, such as insufficiency on analytical process of performance of wind turbine, the predicting inaccuracy, and the deficiency of economic benefit. The maintaining efficiency of generator stator has been improved since the analysis on data gathered from each part by SCADA system of wind power. For the temperature of stator within the generator, the process and analysis of data have been optimized, with the amelioration of Optimal Interclass Variance (OIV) algorithm and the successful monitoring the trends of temperature of stator and its abnormal temperature status. The improved optimal interclass variance algorithm has been proved feasible and efficient, which is capable of processing date of generator stator's temperature curve data and making predictions via neural networks, while improving predicting accuracy of generator stator temperature significantly.