Change detection in smart grids using errors in variables models

Chuanming Wei*, Ami Wiesel, Rick S. Blum

*Corresponding author for this work

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

11 Scopus citations

Abstract

We consider fault detection through apparent changes in the bus susceptance parameters of modern power grids. We formulate the problem using a linear errors-invariables model and derive its corresponding generalized likelihood ratio (GLRT) based on the total least squares (TLS) methodology. Next, we propose a competing detection technique based on the recently proposed total maximum likelihood (TML) framework. We derive the so called TML-GLRT, and show that it can be interpreted as a regularized TLS-GLRT. Numerical simulations in a noisy smart grid setting illustrate the advantages of TML-GLRT over TLS-GLRT with no additional computational costs.

Original languageAmerican English
Title of host publication2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Pages17-20
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 - Hoboken, NJ, United States
Duration: 17 Jun 201220 Jun 2012

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)2151-870X

Conference

Conference2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Country/TerritoryUnited States
CityHoboken, NJ
Period17/06/1220/06/12

Keywords

  • Change detection
  • errors-in-variables
  • generalized likelihood ratio test
  • smart grids
  • total least squares
  • total maximum likelihood

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