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.

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.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Published Version

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