Change detection in smart grids using errors in variables models

Chuanming Wei; Ami Wiesel; Rick S. Blum

doi:10.1109/SAM.2012.6250460

Change detection in smart grids using errors in variables models

Chuanming Wei^*
, Ami Wiesel
, Rick S. Blum

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 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 language	English
Title of host publication	2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Pages	17-20
Number of pages	4
DOIs	https://doi.org/10.1109/SAM.2012.6250460
State	Published - 2012
Event	2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 - Hoboken, NJ, United States Duration: 17 Jun 2012 → 20 Jun 2012

Publication series

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

Conference

Conference	2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012
Country/Territory	United States
City	Hoboken, NJ
Period	17/06/12 → 20/06/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

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

Access to Document

10.1109/SAM.2012.6250460

Cite this

@inproceedings{f97dc1b88acc4bb6b79e048c61200659,

title = "Change detection in smart grids using errors in variables models",

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.",

keywords = "Change detection, errors-in-variables, generalized likelihood ratio test, smart grids, total least squares, total maximum likelihood",

author = "Chuanming Wei and Ami Wiesel and Blum, \{Rick S.\}",

year = "2012",

doi = "10.1109/SAM.2012.6250460",

language = "אנגלית",

isbn = "9781467310710",

series = "Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop",

pages = "17--20",

booktitle = "2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012",

note = "2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012 ; Conference date: 17-06-2012 Through 20-06-2012",

}

Wei, C, Wiesel, A & Blum, RS 2012, Change detection in smart grids using errors in variables models. in 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012., 6250460, Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 17-20, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012, Hoboken, NJ, United States, 17/06/12. https://doi.org/10.1109/SAM.2012.6250460

Change detection in smart grids using errors in variables models. / Wei, Chuanming; Wiesel, Ami; Blum, Rick S.
2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012. 2012. p. 17-20 6250460 (Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Change detection in smart grids using errors in variables models

AU - Wei, Chuanming

AU - Wiesel, Ami

AU - Blum, Rick S.

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AB - 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.

KW - Change detection

KW - errors-in-variables

KW - generalized likelihood ratio test

KW - smart grids

KW - total least squares

KW - total maximum likelihood

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BT - 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012

T2 - 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop, SAM 2012

Y2 - 17 June 2012 through 20 June 2012

ER -

Change detection in smart grids using errors in variables models

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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