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Title: Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint

Abstract

The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1313605
Report Number(s):
NREL/CP-5D00-66689
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the IEEE Global Conference on Signal and Information Processing (GlobalSIP), 7-9 December 2016, Washington, D.C.
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; distributed state estimation; partition; power system; regionalization; spectral clustering

Citation Formats

Wang, Dexin, Yang, Liuqing, Florita, Anthony, Alam, S.M. Shafiul, Elgindy, Tarek, and Hodge, Bri-Mathias. Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint. United States: N. p., 2016. Web. doi:10.1109/GlobalSIP.2016.7905950.
Wang, Dexin, Yang, Liuqing, Florita, Anthony, Alam, S.M. Shafiul, Elgindy, Tarek, & Hodge, Bri-Mathias. Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint. United States. doi:10.1109/GlobalSIP.2016.7905950.
Wang, Dexin, Yang, Liuqing, Florita, Anthony, Alam, S.M. Shafiul, Elgindy, Tarek, and Hodge, Bri-Mathias. 2016. "Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint". United States. doi:10.1109/GlobalSIP.2016.7905950. https://www.osti.gov/servlets/purl/1313605.
@article{osti_1313605,
title = {Automatic Regionalization Algorithm for Distributed State Estimation in Power Systems: Preprint},
author = {Wang, Dexin and Yang, Liuqing and Florita, Anthony and Alam, S.M. Shafiul and Elgindy, Tarek and Hodge, Bri-Mathias},
abstractNote = {The deregulation of the power system and the incorporation of generation from renewable energy sources recessitates faster state estimation in the smart grid. Distributed state estimation (DSE) has become a promising and scalable solution to this urgent demand. In this paper, we investigate the regionalization algorithms for the power system, a necessary step before distributed state estimation can be performed. To the best of the authors' knowledge, this is the first investigation on automatic regionalization (AR). We propose three spectral clustering based AR algorithms. Simulations show that our proposed algorithms outperform the two investigated manual regionalization cases. With the help of AR algorithms, we also show how the number of regions impacts the accuracy and convergence speed of the DSE and conclude that the number of regions needs to be chosen carefully to improve the convergence speed of DSEs.},
doi = {10.1109/GlobalSIP.2016.7905950},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 8
}

Conference:
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