中文摘要：

为了从SCADA数据中获得更明确的物理信息，更好地判断风电机组运行状态，分别将常规平均数法、最小二乘法和该文提出的非参数法（核密度-均值法）用于风电场SCADA数据预处理。建立风电场SCADA数据预处理算法的3个评价指标，包括物理特性一致性、采样时间变化稳健性及采样频率变化稳健性。设计评价指标定量计算公式，用以评价各种预处理算法效果，计算结果表明非参数法（核密度-均值法）能够获得更好的预处理效果。最后，基于核密度-均值法对全工况风电机组SCADA数据进行预处理，分析风电机组运行特性，包括风速与输出功率、轮毂转速的关系以及风能利用系数。

英文摘要：

In order to obtain more explicit physical information from the SCADA data and judge the operating state of wind turbines better, the mean value method, least square method and non-parametric method （kernel density-mean value method） are used to pre-process SCADA data in wind farm. Three evaluation indexes for pre-processing algorithm are presented, including ： （ 1 ） the consistency of physical characteristics ; （2） the robustness of the sampling time ; （3） the robustness of the sampling frequency. The quantitative calculation formulas of the evaluation indexes are designed to evaluate the effect of various kinds of pre-processing algorithms. The results showed that the kernel density-mean value method can obtain better results. Finally, based on kernel density-mean method the SCADA data in full working condition are pre-processed, operating characteristics of wind turbines are analysed, including the relationship between the wind speed and output power, rotational speed of the wind rotor and the wind energy utilization coefficient.