Randomized Dynamic Mode Decomposition for Oscillation Modal Analysis

Abstract

The objective of this research is to identify oscillation modes from real-world measurement data captured by phasor measurement units (PMUs) and make distinction of their characteristics. To this end, dynamic mode decomposition (DMD) is applied. This paper improves DMD performance by data stacking. This enables DMD to accurately identify system eigenvalues and reconstruct signals in the time-evolving format. While data stacking raises the computation cost, we further implement a randomization technique for DMD to radically reduce the size of the data matrix. The randomized DMD (rDMD) is shown to achieve both efficiency in computing and accuracy in mode identification. PMU data from three real-world oscillation events are used for demonstration.