Fast modal identification, monitoring, and visualization for large-scale power systems using Dynamic Mode Decomposition

Abstract

Dynamic Mode Decomposition (DMD) is a relatively new method for simultaneous modal analysis of multiple time-series signals. In this paper, DMD is successfully applied towards transmission-level power system data in an implementation that is able to run quickly. Since power systems are considered as non-linear and time-varying, modal identification is capable of monitoring the evolution of large-scale power system dynamics by providing a breakdown of the constituent oscillation frequencies and damping ratios, and their respective amplitudes. DMD is an efficient algorithm for both off-line and on-line processing of large volumes of time-series measurements, which can enable spatio-temporal analyses, improve situational awareness, and could even contribute towards control strategies. This paper applies DMD on a set of simulated measurements consisting of both frequency and voltage magnitude data. The key advantage of this implementation is its relatively fast computation; for example, it is able to process a 7 s time-window, consisting of 3392 signals with 211 time points, in 0.185 s. Automated processing of transient contingency results, and on-line mode tracking are two proposed applications.