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Title: A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation

Abstract

This paper combines model-based and data-driven methods 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 after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. 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 [2];  [1];  [3]; ORCiD logo [2]
  1. Virginia Polytechnic Institute and State University
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  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:
1602677
Report Number(s):
NREL/CP-5D00-76223
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 IEEE Power & Energy Society General Meeting (PESGM), 4-8 August 2019, Atlanta, Georgia
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; data-driven dynamical systems; dynamic state estimation; Kalman filtering; Koopman mode decomposition; sparse selection; compressed sensing

Citation Formats

Netto, Marcos, Krishnan, Venkat K, Mili, Lamine, Susuki, Yoshihiko, and Zhang, Yingchen. A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation. United States: N. p., 2020. Web. doi:10.1109/PESGM40551.2019.8973772.
Netto, Marcos, Krishnan, Venkat K, Mili, Lamine, Susuki, Yoshihiko, & Zhang, Yingchen. A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation. United States. doi:10.1109/PESGM40551.2019.8973772.
Netto, Marcos, Krishnan, Venkat K, Mili, Lamine, Susuki, Yoshihiko, and Zhang, Yingchen. Thu . "A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation". United States. doi:10.1109/PESGM40551.2019.8973772.
@article{osti_1602677,
title = {A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation},
author = {Netto, Marcos and Krishnan, Venkat K and Mili, Lamine and Susuki, Yoshihiko and Zhang, Yingchen},
abstractNote = {This paper combines model-based and data-driven methods 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 after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level and hence significantly speed up the computational time. 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 = {10.1109/PESGM40551.2019.8973772},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {1}
}

Conference:
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