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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  1/2015 - 13

Anomaly Detection Using Power Signature of Consumer Electrical Devices

CERNAZANU-GLAVAN, C. See more information about CERNAZANU-GLAVAN, C. on SCOPUS See more information about CERNAZANU-GLAVAN, C. on IEEExplore See more information about CERNAZANU-GLAVAN, C. on Web of Science, MARCU, M. See more information about MARCU, M. on SCOPUS See more information about MARCU, M. on SCOPUS See more information about MARCU, M. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (789 KB) | Citation | Downloads: 292 | Views: 329

Author keywords
feature extraction, pattern matching, signal analysis, signal processing

References keywords
power(9), smart(8), signatures(5), energy(5), grid(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 89 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01013
Web of Science Accession Number: 000352158600013
SCOPUS ID: 84924760263

Abstract
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Full text preview
The use of the smart grid for developing intelligent applications is a current trend of great importance. One advantage lies in the possibility of direct monitoring of all devices connected to the electrical network in order to prevent possible malfunctions. Therefore, this paper proposes a method for an automatic detection of the malfunctioning of low-intelligence consumer electrical devices. Malfunctioning means any deviation of a household device from its normal operating schedule. The method is based on a comparison technique, consisting in the correlation between the current power signature of a device and an ideal signature (the standard signature provided by the manufacturer). The first step of this method is to achieve a simplified form of power signature which keeps all the original features. Further, the signal is segmented based on the data provided by an event detection algorithm (values of the first derivatives) and each resulting component is approximated using a regression function. The final step consists of an analysis based on the correlation between the computed regression coefficients and the coefficients of the standard signal. Following this analysis all the differences are classified as a malfunctioning of the analyzed device.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 10]


[2] C. Laughman, K. Lee, R. Cox, S. Shaw, S. Leeb, L. Norford and P. Armstrong, "Power Signature Analysis", Power and Energy Magazine, IEEE, Vol. 1, pp.56-63, 2003.
[CrossRef] [SCOPUS Times Cited 332]


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[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 21]


[4] M. Marcu and C. Cernazanu, "Dynamic Analysis of Electronic Devices' Power Signatures", International Instrumentation and Measurement Technology Conference, I2MTC 2012, Graz, Austria, May 2012.
[CrossRef] [SCOPUS Times Cited 5]


[5] I. C. Miller, "IDDQ testing in deep submicron integrated circuits", Proceedings of International Test Conference, ITC 1999, Atlantic City, USA, Sep. 1999.

[6] K. Fehrenbacher, "10 Monitoring Tools Bringing Smart Energy Home", Business Week, Apr. 2009, [Online] Available: Temporary on-line reference link removed - see the PDF document

[7] J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. S. Reynolds, and S. N. Patel, "Disaggregated End-Use Energy Sensing for the Smart Grid", IEEE Pervasive Computing, Special Issue on Smart Energy Systems, Jan-Mar 2011.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 124]


[8] M. Marcu, C. Stangaciu, A. Topirceanu, D. Volcinschi, and V. Stangaciu, "Wireless Sensors Solution for Energy Monitoring, Analyzing, Controlling and Predicting", Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Volume 57, 2011.
[CrossRef] [SCOPUS Times Cited 5]


[9] Q. Ou, Y. Zhen, X. Li, Y. Zhang, L. Zeng; , "Application of Internet of Things in Smart Grid Power Transmission", Mobile, Ubiquitous, and Intelligent Computing (MUSIC), 2012 Third FTRA International Conference on , pp.96-100, 26-28 June 2012
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 13]


[10] S. J. Huang, C. T. Hsieh, L. K. Kuo, C. W. Lin, C. W. Chang, S. A. Fang, "Classification of home appliance electricity consumption using power signature and harmonic features", Power Electronics and Drive Systems (PEDS), 2011 IEEE Ninth International Conference on , pp.596-599, 5-8 Dec. 2011
[CrossRef] [SCOPUS Times Cited 12]


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[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 54]


[14] T. Hassan, F. Javed, N. Arshad, "An Empirical Investigation of V-I Trajectory Based Load Signatures for Non-Intrusive Load Monitoring", Smart Grid, IEEE Transactions on, vol.5, no.2, pp. 870-878, March 2014
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 45]


[15] A. Abu-Siada, N. Hashemnia, S. Islam, M. Masoum, "Understanding power transformer frequency response analysis signatures", Electrical Insulation Magazine, IEEE , vol.29, no.3, pp. 48-56, May 2013
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[16] M. Marcu, C. Cernazanu, "Applications of Smart Metering and Home Appliances' Power Signatures", Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International , vol., no., pp. 331-335, 12-15 May 2014
[CrossRef] [SCOPUS Times Cited 2]




References Weight

Web of Science® Citations for all references: 237 TCR
SCOPUS® Citations for all references: 683 TCR

Web of Science® Average Citations per reference: 14 ACR
SCOPUS® Average Citations per reference: 40 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-04-18 18:21 in 114 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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