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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Nov 2016
Next issue: Feb 2017
Avg review time: 95 days


PUBLISHER

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: 644266260
doi: 10.4316/AECE


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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2015-Feb-09
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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
 
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Download PDF pdficon (789 KB) | Citation | Downloads: 221 | Views: 859

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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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

[1] R. Kazman, L. Bass, J. Ivers, and G. A. Moreno, "Architecture Evaluation without an Architecture: Experience with the Smart Grid", 33rd International Conference of Software Engineering, May 2011.
[CrossRef] [SCOPUS Times Cited 4]


[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 266]


[3] I. Cepa, Z. Kocur, Z. Muller, "Migration of the IT Technologies to the Smart Grids", ELEKTRONIKA IR ELEKTROTECHNIKA, pp.123-128, Issue 7, 2012
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 15]


[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 2]


[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 50] [SCOPUS Times Cited 91]


[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 3]


[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 4] [SCOPUS Times Cited 3]


[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 6]


[11] W. K. Lee, G. S. K. Fung, H. Y. Lam, F. H. Y. Chan, and M. Lucente, "Exploration on Load Signatures", International Conference on Electrical Engineering, ICEE 2004, Sapporo, Japan, Jul. 2012.

[12] X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler, "Design and Implementation of a High-Fidelity AC Metering Network", The 8th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN’09, 2009, San Francisco, California, USA.

[13] M. Drif, A. J. M. Cardoso, "Stator Fault Diagnostics in Squirrel Cage Three-Phase Induction Motor Drives Using the Instantaneous Active and Reactive Power Signature Analyses", Industrial Informatics, IEEE Transactions on , vol.10, no.2, pp.1348-1360, May 2014
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 24]


[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 9] [SCOPUS Times Cited 13]


[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
[CrossRef] [SCOPUS Times Cited 28]


[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 Record]




References Weight

Web of Science® Citations for all references: 93 TCR
SCOPUS® Citations for all references: 455 TCR

Web of Science® Average Citations per reference: 5 ACR
SCOPUS® Average Citations per reference: 27 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 2016-12-05 00:41 in 66 seconds.




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


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