PMU-based Reduced-order Modeling of Power System Dynamics via Selective Modal Analysis

This paper investigates how to perform online system identification employing synchrophasor data. Two approaches to identifying a reduced-order model are presented: a purely data-driven approach, and an approach that integrates online data-driven dynamic system identification with first-principle offline selective modal analysis. With prior knowledge of the frequency range interesting to power system operators, it is shown that the second approach recovers the key modes of the original system and produces a much reduced-order model of grid-level dynamics. Even with the presence of uncertainty about the actual modes of interest, an automatic tuning scheme is devised to adaptively adjust the frequency range to improve system identification. Numerical examples with synthetic synchrophasor data demonstrate the efficacy of the proposed identification approach.