Stochastic subspace identification-based approach for tracking inter-area oscillatory modes in bulk power system utilising synchrophasor measurements

Stochastic subspace identification (SSI) methods have been widely employed for oscillatory mode identification on probing and ambient data and are reported to have good performances. This work proposes a novel SSI-based approach for identifying dominant oscillatory mode from measurement data and extends the application of SSI to ringdown condition. The proposed approach first constructs an initial cluster of eigenvalues from SSI with repetitive calculations and then utilises a novel hierarchical clustering method to extract the dominant modes from the initial cluster. The repetitive calculations within the SSI are performed through varying the model order over a range defined by a novel initial order determination process. By doing so the challenge of model order determination for SSI-based methods is resolved. Moreover, benefiting from the repetitive calculations and the clustering process, the proposed approach is highly immune to prevalent noises in the measurements. Finally, the proposed approach is applied and validated on the field-measurement data from the phasor measurement units of China Southern Power Grid (CSG) through comparisons with Prony, ARMAX, and Monte Carlo methods. Test results demonstrate that the proposed approach performs with high accuracy, robustness, and efficiency in CSG.