【摘要】 针对现有方法模型简单、气象因素考虑不足和缺少异常数据检测等问题，提出一种基于相对密度离群因子（relative RDOF）异常检测和支持向量回归（SVR）组合的场地校准模型。首先，通过RDOF与四分位法对测风塔与风电机组距下垫面相同高度风速间的风速比进行异常数据检测，间接实现风速预处理；同时，根据测风塔与风电机组相同高度风切变序列的相关性，动态引入大气稳定度等级，与多种常规自然因素共同作为模型输入。然后，建立SVR场地校准模型修正风速。最后，通过算例验证，并与国际电工委员会（IEC）标准方法进行对比。结果表明，该方法可有效提升场地校准模型的风速修正精度。更多还原

【Abstract】 Aiming at the problems of simple model,insufficient consideration of meteorological factors and lack of abnormal detection,a site calibration model based on relative density outlier factor(relative density outlier factor,RDOF)anomaly detection and support vector regression(support vector regression,SVR)is proposed. Firstly,the RDOF and quartile methods are used to detect the abnormal data of the wind speed ratio at the same height between the wind tower and the wind turbine from the underlying surface,and the wind speed preprocessing is indirectly realized. At the same time,according to the correlation between the wind shear sequence at the same height of the wind tower and the wind turbine,the atmospheric stability grade is dynamically introduced,and a variety of conventional natural factors are used as model input. Then,the SVR site calibration model is established to correct the wind speed. Finally,it is verified by a calculation example and compared with the standard method of the International Electrotechnical Commission(IEC). The results show that the method can effectively improve the accuracy of wind speed correction of site calibration model.更多还原