A stochastic subspace identification (SSI) method based on data-driven is applied to identification of power system low frequency oscillation modes. Firstly, by means of wavelet transform the noise contained in each signal is eliminated, and then the DC component is also eliminated. Hankel matrix is built based on treated data. The state space model is computed by QR method, singular value decomposition and Kalman filter estimation. Finally the system low frequency oscillation modes parameters are obtained by the state matrix eigenvalues. The ideal signal, simulated signal and the WAMS data of power system are analyzed respectively by the method. Analysis results show that SSI method based on data-driven can identify the main oscillation of the system accurately and it can be applied to on-line identification of power system oscillation modes.% 根据WAMS实测数据，对电力系统低频振荡模式进行辨识，对基于数据驱动随机子空间(SSI)辨识方法进行了研究。首先通过小波技术消去信号中的噪声分量，然后消去直流分量。利用处理后的数据构造Hankel矩阵，通过QR分解、SVD分解，利用卡尔曼滤波估计得到系统的随机状态模型，再对状态矩阵进行特征值分解，最终得到系统低频振荡模式参数。利用该方法分别对理想信号、仿真信号、电力系统实测数据进行分析。分析结果表明，基于数据驱动随机子空间方法能够准确辨识出系统主导振荡模式，可以应用于低频振荡模式的在线辨识。
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