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由于同一风速下的风电功率存在较大范围的波动,传统风电功率曲线建模方法难以获得较高的精度.提出一种基于最优平滑阶数的风电功率曲线建模策略,首先基于时间序列平滑的预处理方法得到新的输入风速,并以相关系数最大为目标函数选择最优的平滑阶数,再利用 BP 神经网络拟合得到风电功率曲线.基于西南地区某风电场单机实测数据的仿真研究表明,以最优平滑预处理得到的风速为输入建立的风电功率曲线模型精度显著高于已有方法中以原始风速为输入建立的风电功率曲线模型.“,”It is difficult for traditional modeling methods of wind power to get higher accuracy due to the large range fluctuation of wind power under the condition of same wind speed.A modeling strategy of wind power curve based on optimal smoothing order is proposed.Firstly,smoothing preprocessing method based on time series is utilized to a-chieve new input wind speed,optimal smoothing order with maximum correlation coefficient is chosen as objective func-tion,and BP neural network is applied to fit wind power curve.Based on the measured data of the wind turbine in a wind farm located in southwest China,experimental results show that the accuracy of wind power curve model built by taking wind speed as input obtained by the optimal smoot-hing preprocessing is obviously better than that of the exist-ing methods which utilize original wind speed data as input.