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Title: Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

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.

Authors:
 [1]; ; ;
  1. Iowa State University
Publication Date:
Research Org.:
Iowa State Univ., Ames, IA (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1572409
DOE Contract Number:  
OE0000876
Resource Type:
Conference
Journal Name:
2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA
Additional Journal Information:
Conference: 2019 IEEE Power & Energy Society General Meeting, Atlanta, GA, USA, August 4-8, 2019
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION

Citation Formats

Sharma, Pranav, Huang, Bowen, Ajjarapu, Venkatramana, and Vaidya, Umesh. Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators. United States: N. p., 2019. Web.
Sharma, Pranav, Huang, Bowen, Ajjarapu, Venkatramana, & Vaidya, Umesh. Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators. United States.
Sharma, Pranav, Huang, Bowen, Ajjarapu, Venkatramana, and Vaidya, Umesh. Tue . "Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators". United States.
@article{osti_1572409,
title = {Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators},
author = {Sharma, Pranav and Huang, Bowen and Ajjarapu, Venkatramana and Vaidya, Umesh},
abstractNote = {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.},
doi = {},
journal = {2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Conference:
Other availability
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