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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
Next issue: Nov 2018
Avg review time: 80 days


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

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


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k-Degree Anonymity Model for Social Network Data Publishing, MACWAN, K. R., PATEL, S. J.
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Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis, PANIGRAHY, P. S., CHATTOPADHYAY, P.
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A Brief Review on the Validity and Reliability of Microsoft Kinect Sensors for Functional Assessment Applications, DIAZ-MONTERROSAS, P. R., POSADA-GOMEZ, R., MARTINEZ-SIBAJA, A., AGUILAR-LASSERRE, A. A., JUAREZ-MARTINEZ, U., TRUJILLO-CABALLERO, J. C.
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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2010 - 16

Cluster Classification of Partial Discharges in Oil-impregnated Paper Insulation

SURESH, S. D. R. See more information about SURESH, S. D. R. on SCOPUS See more information about SURESH, S. D. R. on IEEExplore See more information about SURESH, S. D. R. on Web of Science, USA, S. See more information about USA, S. on SCOPUS See more information about USA, S. on SCOPUS See more information about USA, S. on Web of Science
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Download PDF pdficon (668 KB) | Citation | Downloads: 1,325 | Views: 3,310

Author keywords
PDs, multiple PD sources, oil-impregnated paper insulation, PD classification, cluster analysis

References keywords
partial(11), discharge(9), insulation(7), power(6), transformer(5), pattern(5), recognition(4), fuzzy(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 90 - 93
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01016
Web of Science Accession Number: 000275458900016
SCOPUS ID: 77954667129

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Recognition of multiple partial discharge (PD) sources in high voltage equipment has been a challenging task until now. The work reported here, aims to recognize multiple PD sources in oil-impregnated paper using Cluster Analysis (CA) and Fuzzy Logic (FL). The typical sources of PD in transformer are identified and the corresponding single source PD defect laboratory models are fabricated. From the measured PD signals, the necessary statistical parameters are extracted by applying CA for classification. A Fuzzy based algorithm has been developed to recognize single source PDs. The developed algorithm has also been applied to recognize multiple PD sources.

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

[1] L. E. Lundgaard, W. Hansen, D. Linhjell, T. J. Painter, "Aging of oil-impregnated paper in power transformers", IEEE Transactions on Power Delivery, Vol. 19, pp. 230-239, 2004
[CrossRef] [Web of Science Times Cited 264] [SCOPUS Times Cited 404]

[2] F. H. Kreuger et. al., "Classification of Partial Discharges", IEEE Transactions on Electrical Insulation, Vol. 28, No. 6, pp. 917-931, December 1993
[CrossRef] [Web of Science Times Cited 155] [SCOPUS Times Cited 207]

[3] A. Krivda and D. Birtwhistle, "Recognition of Multiple Partial Discharge Patterns", High Voltage Engineering Symposium, 22-27 August 1999 Conference Publication No. 467, IEE, 1999

[4] D. Suresh, "Feature Extraction for Multi Source Partial Discharge Pattern Recognition", IEEE Indicon 2005 Conference, Chennai, India, 1-13 Dec. 2005

[5] A. Cavallini, G. C. Montanari, and F. Puletti, "Partial Discharge Analysis and Asset Management: Experiences on Monitoring of Power Apparatus", IEEE PES Transmission and Distribution Conference and Exposition, Latin America, Venezuela, 2006
[CrossRef] [SCOPUS Times Cited 5]

[6] T. K. Abdel-Galil, R. M. Sharkawy, M. M. A. Salama and R. Bartinikas, "Partial Discharge Pattern Classification Using the Fuzzy Decision Tree Approach", IEEE Transactions on Instrumentation and Measurement, Vol. 54, No. 6, pp. 2258-2263, December 2005
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 46]

[7] S. M. Strachan, S. Rudd, S. D. J. McArthur and M. D. Judd, "Knowledge-Based Diagnosis of Partial Discharges in Power Transformers", IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 15, No. 1, February 2008
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 68]

[8] Tadeja Babnik, Raj K. Aggarwal and Philip J. Moore, "Principal Component and Hierarchical Cluster Analyses as Applied to Transformer Partial Discharge Data With Particular Reference to Transformer Condition Monitoring", IEEE Transactions on Power Delivery, 2008
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 27]

[9] C. R. Li, Wei Wang, Z. G. Tang, Y. S. Ding "PD Pattern Recognition in Transformer by Using UHF Technology", Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, Indianapolis, IN USA, 19-22, September 2004
[CrossRef] [Web of Science Record]

[10] Jiang Lei, Jin Xianhe, "Partial Discharge Pattern Recognition of Insulation Models of Power Transformer", Proceedings of the 6th International Conference on Properties and Applications of Dielectric Materials, Xi'an Jiaotong University, China, June 21-26, 2000

[11] Feliciano Massingue, Sander Meijer, Pantelis D. Agoris, Johan J. Smit and Jose Lopez-Roldan, "Partial Discharge Pattern Analysis of Modeled Insulation Defects in Transformer Insulation", Conference Record of the IEEE International Symposium on Electrical Insulation, 2006
[CrossRef] [Web of Science Times Cited 1]

[12] F. H. Kreuger, "Partial Discharge Detection in High-Voltage Equipment", Butterworth & Co, 1989 [PermaLink]

[13] IEC 60270, Partial Discharge Measurements, 3rd edition, March 2001

[14] J. R. Quinlan, "C4.5 Programs for Machine Learning", Morgan Kaufmann, 1993 [PermaLink]

[15] L. O. Hall and P. Lande, "Generating fuzzy rules from data", Proc. 5th IEEE Int. Conf. Fuzzy Systems, vol. 3, pp. 1757-1762, Sep. 8-11, 1996

References Weight

Web of Science® Citations for all references: 526 TCR
SCOPUS® Citations for all references: 757 TCR

Web of Science® Average Citations per reference: 35 ACR
SCOPUS® Average Citations per reference: 50 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 2018-09-21 13:59 in 64 seconds.

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

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