Investigating the Significance of Dynamic Mode Decomposition for Fast and Accurate Parameter Estimation in Power Grids

Abstract

Due to harmonics, sub-harmonics, and inter-harmonics in modern electrical grid, resolution of the estimation of parameters like frequency and amplitude plays a vital role in determining the stability of the system. Fast and accurate estimation of parameters with a minimal number of data points is essential for quick real-time action in case of contingency. Dynamic Mode Decomposition (DMD) is one of the recently proposed data-driven methods used to estimate the frequency/amplitude with high-resolution, even though it is a spatiotemporal data analytics tool originated in fluid dynamics. This paper investigates the significance of DMD for a fast and accurate estimation of electric parameters with a minimal number of data points. Further, DMD is compared with DFT and Prony algorithm for electric parameter estimation based on the number of samples required for accurate estimation. This aspect is not considered so far.