Data-driven modeling of power networks
Systems and Control
2021-04-15 v1 Systems and Control
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.
Cite
@article{arxiv.2104.06478,
title = {Data-driven modeling of power networks},
author = {Bita Safaee and Serkan Gugercin},
journal= {arXiv preprint arXiv:2104.06478},
year = {2021}
}