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Title: Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation

Abstract

Model-based and data-driven methods are combined to develop a hierarchical decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.

Authors:
 [1]; ORCiD logo [1];  [2];  [3];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Virginia Polytechnic Institute and State University
  3. Osaka Prefecture University
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1496056
Report Number(s):
NREL/PO-5D00-73236
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at Operator Theoretic Methods in Dynamic Data Analysis and Control, 11-15 February 2019, Los Angeles, California
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; data-driven dynamical systems; dynamic state estimation; Kalman filtering; Koopman operator; Koopman mode decomposition

Citation Formats

Netto, Marcos, Krishnan, Venkat K, Mili, Lamine, Susuki, Yoshihiko, and Zhang, Yingchen. Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation. United States: N. p., 2019. Web.
Netto, Marcos, Krishnan, Venkat K, Mili, Lamine, Susuki, Yoshihiko, & Zhang, Yingchen. Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation. United States.
Netto, Marcos, Krishnan, Venkat K, Mili, Lamine, Susuki, Yoshihiko, and Zhang, Yingchen. Tue . "Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation". United States. https://www.osti.gov/servlets/purl/1496056.
@article{osti_1496056,
title = {Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation},
author = {Netto, Marcos and Krishnan, Venkat K and Mili, Lamine and Susuki, Yoshihiko and Zhang, Yingchen},
abstractNote = {Model-based and data-driven methods are combined to develop a hierarchical decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists of robust, model-based, decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data-driven DSE. The proposed hybrid framework does not depend on the centralized infrastructure of the control centers; thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator locations. Also, the network model is not necessary; thus, a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on the 2,000-bus synthetic Texas system and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.},
doi = {},
url = {https://www.osti.gov/biblio/1496056}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {2}
}

Conference:
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