Estimation of Power System Inertia Using Nonlinear Koopman Modes

TL;DR

This paper introduces a novel method using Koopman Mode Decomposition to estimate power system inertia directly from dynamic time-series data, applicable even in nonlinear regimes, and validates it through simulations.

Contribution

It presents a new nonlinear data-driven approach for power system inertia estimation using Koopman Mode Decomposition, extending applicability to nonlinear dynamics.

Findings

Effective in nonlinear regimes

Validated with IEEE test system simulations

Provides direct inertia estimates from data

Abstract

We report a new approach to estimating power system inertia directly from time-series data on power system dynamics. The approach is based on the so-called Koopman Mode Decomposition (KMD) of such dynamic data, which is a nonlinear generalization of linear modal decomposition through spectral analysis of the Koopman operator for nonlinear dynamical systems. The KMD-based approach is thus applicable to dynamic data that evolve in nonlinear regime of power system characteristics. Its effectiveness is numerically evaluated with transient stability simulations of the IEEE New England test system.