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Detection of energy theft and defective smart meters in smart grids using linear regression

The utility providers are estimated to lose billions of dollars annually due to energy theft. Although the implementation of smart grids offers technical and social advantages, the smart meters deployed in smart grids are susceptible to more attacks and network intrusions by energy thieves as compar...

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Main Authors:	Yip, S.C., Wong, K., Hew, W.P., Gan, M.T., Phan, R.C.W., Tan, S.W.
Format:	Article
Published:	Elsevier 2017
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/17579/ https://doi.org/10.1016/j.ijepes.2017.04.005
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id	my.um.eprints.17579
record_format	eprints
spelling	my.um.eprints.175792017-07-31T06:07:58Z http://eprints.um.edu.my/17579/ Detection of energy theft and defective smart meters in smart grids using linear regression Yip, S.C. Wong, K. Hew, W.P. Gan, M.T. Phan, R.C.W. Tan, S.W. TA Engineering (General). Civil engineering (General) The utility providers are estimated to lose billions of dollars annually due to energy theft. Although the implementation of smart grids offers technical and social advantages, the smart meters deployed in smart grids are susceptible to more attacks and network intrusions by energy thieves as compared to conventional mechanical meters. To mitigate non-technical losses due to electricity thefts and inaccurate smart meters readings, utility providers are leveraging on the energy consumption data collected from the advanced metering infrastructure implemented in smart grids to identify possible defective smart meters and abnormal consumers’ consumption patterns. In this paper, we design two linear regression-based algorithms to study consumers’ energy utilization behavior and evaluate their anomaly coefficients so as to combat energy theft caused by meter tampering and detect defective smart meters. Categorical variables and detection coefficients are also introduced in the model to identify the periods and locations of energy frauds as well as faulty smart meters. Simulations are conducted and the results show that the proposed algorithms can successfully detect all the fraudulent consumers and discover faulty smart meters in a neighborhood area network. Elsevier 2017 Article PeerReviewed Yip, S.C. and Wong, K. and Hew, W.P. and Gan, M.T. and Phan, R.C.W. and Tan, S.W. (2017) Detection of energy theft and defective smart meters in smart grids using linear regression. International Journal of Electrical Power & Energy Systems, 91. pp. 230-240. ISSN 0142-0615 https://doi.org/10.1016/j.ijepes.2017.04.005 DOI: 10.1016/j.ijepes.2017.04.005
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Yip, S.C. Wong, K. Hew, W.P. Gan, M.T. Phan, R.C.W. Tan, S.W. Detection of energy theft and defective smart meters in smart grids using linear regression
description	The utility providers are estimated to lose billions of dollars annually due to energy theft. Although the implementation of smart grids offers technical and social advantages, the smart meters deployed in smart grids are susceptible to more attacks and network intrusions by energy thieves as compared to conventional mechanical meters. To mitigate non-technical losses due to electricity thefts and inaccurate smart meters readings, utility providers are leveraging on the energy consumption data collected from the advanced metering infrastructure implemented in smart grids to identify possible defective smart meters and abnormal consumers’ consumption patterns. In this paper, we design two linear regression-based algorithms to study consumers’ energy utilization behavior and evaluate their anomaly coefficients so as to combat energy theft caused by meter tampering and detect defective smart meters. Categorical variables and detection coefficients are also introduced in the model to identify the periods and locations of energy frauds as well as faulty smart meters. Simulations are conducted and the results show that the proposed algorithms can successfully detect all the fraudulent consumers and discover faulty smart meters in a neighborhood area network.
format	Article
author	Yip, S.C. Wong, K. Hew, W.P. Gan, M.T. Phan, R.C.W. Tan, S.W.
author_facet	Yip, S.C. Wong, K. Hew, W.P. Gan, M.T. Phan, R.C.W. Tan, S.W.
author_sort	Yip, S.C.
title	Detection of energy theft and defective smart meters in smart grids using linear regression
title_short	Detection of energy theft and defective smart meters in smart grids using linear regression
title_full	Detection of energy theft and defective smart meters in smart grids using linear regression
title_fullStr	Detection of energy theft and defective smart meters in smart grids using linear regression
title_full_unstemmed	Detection of energy theft and defective smart meters in smart grids using linear regression
title_sort	detection of energy theft and defective smart meters in smart grids using linear regression
publisher	Elsevier
publishDate	2017
url	http://eprints.um.edu.my/17579/ https://doi.org/10.1016/j.ijepes.2017.04.005
_version_	1643690457563136000
score	13.211869

Detection of energy theft and defective smart meters in smart grids using linear regression

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