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中文摘要：
针对并网风力机组运行时非线性、耦合性和大惯性的特点，提出了一种基于样本修整和支持向量机算法的系统辨识方法，并通过实例将该方法与单纯的支持向量机算法、BP （back propagation）神经网络算法进行比较。结果表明，样本修整后与修整前相比，训练速度和预测精度都有明显提高，基于样本修整和支持向量机算法的辨识方法具有明显的优越性。
英文摘要：
In view of that the operation characteristics of wind generation unit are nonlinear, coupled and with great inertia,a new system identification method based on sample modification and support vector machine （SVM）was put forward.By virtue of an application case,the proposed method of modified support vector machine was compared with the traditional SVM method and the back propagation （BP）neural network algorithm.The results indicate that,it is faster and more accurate than the initial one due to using modified samples.The study on the actual operation characteristics can provide reference to precise modeling and intelligent control of wind generation units.
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