Data-driven modeling of power networks

Abstract

We develop a non-intrusive data-driven modeling framework for power network dynamics using the Lift and Learn approach of [30]. A lifting map is applied to the snapshot data obtained from the original nonlinear swing equations describing the underlying power network such that the lifteddata corresponds to quadratic nonlinearity. The lifted data is then projected onto a lower dimensional basis and the reduced quadratic matrices are fit to this reduced lifted data using a least-squares measure. The effectiveness of the proposed approach is investigated by two power network models.