  In this paper, we propose linear operator theoretic framework involving
Koopman operator for the data-driven identification of power system dynamics.
We explicitly account for noise in the time series measurement data and propose
robust approach for data-driven approximation of Koopman operator for the
identification of nonlinear power system dynamics. The identified model is used
for the prediction of state trajectories in the power system. The application
of the framework is illustrated using an IEEE nine bus test system.
