基于风电SCADA数据定子温度的预处理算法

针对风电机组各部件性能分析过程繁琐低效、预测精度不高以及经济效益不足的非正常风电机组状态问题，提出一种基于风电SCADA数据定子温度的预处理算法.通过分析风电SCADA系统采集的各部件数据，对于其中发电机的定子温度，优化数据处理与分析的过程，改进现有的最优组内方差预处理算法，监测定子温度的趋势与非正常温度的状态，提高了对发电机定子的维修效率.通过实例分析表明改进后的最优组方差算法可行且高效，能够准确处理发电机定子温度曲线数据并通过使用神经网络进行预测，显著提高了风电机组中发电机定子温度预测的准确性.

Preprocessing Algorithm of Stator Temperature Based on Wind Farm SCADA Data

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.