Estimation of Power System Inertia Using Nonlinear Koopman Modes
For power system operators, this provides a new tool for inertia estimation from dynamic data without requiring linear assumptions.
This paper introduces a Koopman Mode Decomposition-based method to estimate power system inertia from time-series data, applicable to nonlinear dynamics. Numerical tests on the IEEE New England system demonstrate its effectiveness.
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.