  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.
