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研究了一类Hammerstein有限脉冲响应模型的建模方法,并用于风电场弃风电量预测领域.采用极大似然估计律对似然方程进行最小化,同时为了减少有色噪声对建模过程的干扰,结合极大似然估计方法和滤波过程,将原本耦合的非线性模型转变为独立参数的辨识模型,进而推导了一类基于滤波的极大似然随机梯度辨识算法,并将该方法用于风电场弃风电量的预测领域.仿真结果表明提出的算法可以精确的辨识实际风电场的风电功率特性曲线,并能很好的预测风电场的弃风电量情况,具有很强的实用性.“,”The parameter estimation problem of Hammerstein finite impulse response models and its application on the wind curtailment prediction field were considered.By adopting the maximum likelihood principle,the maximum likelihood estimate was obtained by minimizing the likelihood function.To reduce the impact of the unknown noise term,the maximum likelihood idea and the filtering theory were combined by changing the coupled nonlinear model into a parameter-independent model and to derive a filtering based maximum likelihood stochastic gradient algorithm for the Hammerstein system modeling on wind power curtailment prediction.The given simulation validates that the proposed algorithm can identify the wind power characteristic curve accurately and contributes to calculate the wind power curtailment prediction that shows its good practicability.