Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators
Signal Processing
2019-03-19 v1 Systems and Control
Abstract
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.
Keywords
Cite
@article{arxiv.1903.06828,
title = {Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators},
author = {Pranav Sharma and Bowen Huang and Umesh Vaidya and Venkatramana Ajjarapu},
journal= {arXiv preprint arXiv:1903.06828},
year = {2019}
}
Comments
Accepted for publication in IEEE Power and Energy System General Meeting 2019