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


Stochastic subspace identification (SSI) strategies have been widely used for oscillatory mode identification on probing and ambient knowledge and are reported to possess good performances. This work proposes a novel SSI-primarily based approach for identifying dominant oscillatory mode from measurement information and extends the application of SSI to ringdown condition. The proposed approach initial constructs an initial cluster of eigenvalues from SSI with repetitive calculations and then utilises a novel hierarchical clustering methodology 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 outlined by a completely unique initial order determination process. By doing therefore the challenge of model order determination for SSI-based methods is resolved. Moreover, cashing in on the repetitive calculations and also the clustering process, the proposed approach is extremely proof against prevalent noises within the measurements. Finally, the proposed approach is applied and validated on the sphere-measurement knowledge from the phasor measurement units of China Southern Power Grid (CSG) through comparisons with Prony, ARMAX, and Monte Carlo methods. Check results demonstrate that the proposed approach performs with high accuracy, robustness, and potency in CSG.

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