A Novel Dominant Mode Estimation Method for Analyzing Inter-Area Oscillation in China Southern Power Grid

This paper proposes a new approach to estimate dominant mode for monitoring inter-area oscillation in the China Southern power grid (CSG) by the use of phasor measurement units (PMUs) under both ringdown and ambient conditions. The state space model is identified by the data driven stochastic subspace identification (Data-SSI) algorithm. The canonical variate algorithm is used first to construct the weighted projection matrix of the Data-SSI. Then, the criterion for model order selection is developed to estimate the model order, and the linear model of power system is built with Data-SSI. The dominant oscillation modes are calculated by eigenvalue analysis. To accurately identify the dominant modes, repetitive results are calculated with model order variation, and then clustering analysis and stepwise refinement are applied to discriminating the dominant modes from trivial ones to improve the estimation accuracy. Field-measurement data collected by PMUs in CSG is used to validate the proposed algorithm. The comparison between existing mode estimation techniques and the proposed approach demonstrates its accuracy and robustness under both ringdown and ambient conditions.