Campus Units
Electrical and Computer Engineering
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2019
Journal or Book Title
arXiv
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.
Copyright Owner
The Authors
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Sharma, Pranav; Huang, Bowen; Vaidya, Umesh; and Ajjarapu, Venkataramana, "Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators" (2019). Electrical and Computer Engineering Publications. 214.
https://lib.dr.iastate.edu/ece_pubs/214
Comments
This is a pre-print of the article Sharma, Pranav, Bowen Huang, Umesh Vaidya, and Venkatramana Ajjarapu. "Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators." arXiv preprint arXiv:1903.06828 (2019). Posted with permission.