Blind estimation of states and topology (BEST) in power systems

Idan Gera, Yair Yakoby, Tirza Routtenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we consider the problem of state estimation and topology identification in power systems. We assume the DC model of real power measurements with unknown voltage phases and an unknown admittance matrix. We show that this problem is equivalent to the blind source separation (BSS) problem, where the mixing matrix is a weighted Laplacian matrix. We propose two new Blind Estimation of States and Topology (BEST) methods for this problem. The first method, Cov-BEST, is based on utilizing the states' second-order statistics and the positive-definiteness of the reduced Laplacian matrix. The second method, Generalized Laplacian Separation (GLS)-BEST, is obtained by applying any general BSS method, followed by an approach that resolves the inherent BSS ambiguities by utilizing the Laplacian matrix properties. In contrast to existing methods, the proposed methods achieve full recovery of the topology matrix and are not limited to matrix eigenvectors estimation. The performance of the proposed methods is evaluated for a general network with an arbitrary number of buses and for the IEEE-14 bus system, and compared with the oracle state estimator.

Original languageEnglish
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages1080-1084
Number of pages5
ISBN (Electronic)9781509059904
DOIs
StatePublished - 7 Mar 2018
Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
Duration: 14 Nov 201716 Nov 2017

Publication series

Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume2018-January

Conference

Conference5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Country/TerritoryCanada
CityMontreal
Period14/11/1716/11/17

Keywords

  • Blind source separation (BSS)
  • Laplacian mixing matrix
  • Power system monitoring
  • State estimation
  • Topology identification

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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