Online Identification of Low-Frequency Oscillation Based on Principal Component Analysis Subspace Tracking Algorithm

The authors propose a new principal component analysis subspace tracking algorithm for analysis of low-frequency oscillations. This algorithm has the merits of prony algorithm that it can get oscillation frequency, attenuation, amplitude and phase of the system from the data, which being measured now. At the same time, because the subspace doesn´t require eigenvalue decomposition of the sample correlation matrix or singular value decomposition of the data matrix, the calculation time is reduced. The results of simulation of the model of low-frequency oscillation validate the feasibility and effectiveness of the proposed method.