Data-driven modeling of power networks

TL;DR

This paper introduces a non-intrusive, data-driven modeling framework for power network dynamics using the Lift and Learn approach, transforming nonlinear swing equations into a quadratic form for efficient reduced-order modeling.

Contribution

It applies the Lift and Learn method to power network models, enabling quadratic representation and reduced-order modeling of complex nonlinear dynamics.

Findings

Effective modeling of power network dynamics using the proposed approach

Successful application to two different power network models

Demonstrated reduction in computational complexity

Abstract

We develop a non-intrusive data-driven modeling framework for power network dynamics using the Lift and Learn approach of \cite{QianWillcox2020}. A lifting map is applied to the snapshot data obtained from the original nonlinear swing equations describing the underlying power network such that the lifted-data 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.