Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators
For power system engineers, it offers a noise-robust approach to model and predict system dynamics without explicit physical models.
This paper proposes a robust data-driven method using Koopman operator theory to identify and predict nonlinear power system dynamics from noisy measurements, demonstrated on an IEEE 9-bus system.
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.