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电力系统中的功率振荡根据其产生机理的不同可以分为负阻尼振荡和强迫功率振荡。虽然这两种功率振荡形式比较接近，但对其采用的控制措施却完全不同。因此如何根据广域测量系统的实时数据来区分功率振荡的类型成为了采取合适措施抑制功率振荡的前提条件。基于此，以支持向量机方法作为工具，提出了一种通过辨识实时功率振荡曲线来区分其振荡性质的实用方法。针对2种功率振荡的起因与特点，该方法采用希尔伯特-黄变换求取振荡曲线主导模式的包络线，并在该包络线上等间距选取100个采样点作为样本对支持向量机的神经网络进行训练和测试。以16机68节点系统功率振荡仿真曲线为训练样本，训练得到了用于功率振荡类型区分的支持向量机模型。并将其应用于16机68节点系统和实际大规模区域电网的振荡类型区分，分析结果表明所提方法能够准确地区分振荡类型，具有工程实际应用价值。“,”At present stage, active power oscillation of electric system can be classified into two categories—negative damping and forced power oscillation according to its generation mechanism. Although the two power oscillation modes are similar, their control measures needs to be adopted are totally different. Therefore, distinction of the two oscillation types becomes precondition for suppressing the oscillation with proper measures. This paper proposes a practical approach to recognize oscillation types by identification of real-time power oscillation curves recorded by WAMS. Hilbert-Huang transform is employed to obtain envelope curve of power oscillation curve, based on which 100 sampling points are selected to train and test the neural network supporting vector machine. A supporting vector machine for identifying the characteristic of power oscillations is trained by simulation data of 16 machines 68 nodes power system. Then, this paper applies this supporting vector machine to identify the power oscillation curves from 16 machines 68 nodes power system and real power grid. All tests indicate that the proposed oscillation recognition method possesses good precision and is provided with practical engineering application.