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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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LATEST NEWS

2018-Jun-27
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.

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.

2017-Feb-16
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.

Read More »


    
 

  3/2010 - 3

A New Protection Scheme for High Impedance Fault Detection using Wavelet Packet Transform

GHAFFARZADEH, N. See more information about GHAFFARZADEH, N. on SCOPUS See more information about GHAFFARZADEH, N. on IEEExplore See more information about GHAFFARZADEH, N. on Web of Science, VAHIDI, B. See more information about VAHIDI, B. on SCOPUS See more information about VAHIDI, B. on SCOPUS See more information about VAHIDI, B. on Web of Science
 
Click to see author's profile in 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 (540 KB) | Citation | Downloads: 1,128 | Views: 4,649

Author keywords
artificial neural network, distribution networks, fault detection, high impedance fault, wavelet packet

References keywords
power(22), high(15), fault(15), impedance(14), detection(14), delivery(12), wavelet(6), distribution(6), system(4), networks(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-08-31
Volume 10, Issue 3, Year 2010, On page(s): 17 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.03003
Web of Science Accession Number: 000281805600003
SCOPUS ID: 77956620776

Abstract
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This paper proposed a novel technique to effectively discriminate between the HIF and the normal system operation events in distribution by combining a preprocessing module based on wavelet packet transform with an artificial neural network(ANN). Wavelet packet is firstly applied to extract of distinctive feature of current signals. Then this information is introduced to training ANN for identifying an HIF from the normal system operation events. The simulated results clearly show that the proposed technique can accurately identify the HIF in overhead distribution feeder.


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

[1] M. A. Aucoin, and B. D. Russel, "Distribution high impedance Detection utilizing high frequency components", IEEE Transaction on Power Apparatus and System, vol. 101, no. 6, pp. 1596-1606, 1982.
[CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 112]


[2] M. A. Aucoin, B. D. Russel, and C. L. Benner, "High impedance fault Detection for industrial power systems", in Proc. of IEEE Industry Applications Society Annual Meeting, vol. 2, pp. 1788-1792, 1989.
[CrossRef] [Web of Science Times Cited 1]


[3] M. A. Aucoin, "Status of high impedance fault detection", IEEE Transaction on Power Apparatus and System, vol. 104, no. 3, pp. 638-643, 1985.
[CrossRef] [SCOPUS Times Cited 4]


[4] C. G. Wester, "High impedance fault detection on distribution systems", In Proc. Of 1998 Rural Electric Power Conference, pp. c5-1-5, 1998.
[CrossRef]


[5] S. Ebron, D. L. Lubkeman, and M. White, "A neural network approach to the detection of incipient faults on power distribution feeders", IEEE Trans. on Power Delivery, vol. 5, no. 2, pp. 905-914, 1990.
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 140]


[6] A. F. Sultan, G. W. Swift, and D. J. Fedirchuk, "Detecting arcing downed -wires using fault current flicker and half cycle asymmetry", IEEE Transactions on Power Delivery, vol. 9, no. 1, pp. 461-470, 1994.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 81]


[7] D. Jeering, and J. R. Linders, "Ground resistive-revisited", IEEE Transactions on Power Delivery, vol. 4, no. 2, pp. 949-956, 1989.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 12]


[8] P. R. Silva, A. Santos, W. C. Boaventura, G. C. Miranda, and J. A. Scott, "Impulse response analysis of a real feeder for high impedance fault detection", in Proc. of 1994 IEEE Int. Conf. on Transmission and distribution, pp. 276-283, 1994.
[CrossRef] [Web of Science Times Cited 5]


[9] W. H. Kown, G. W. Lee, Y. M. Park, M. C. Yoon, and M. H. Yoo, "High impedance fault detection utilizing incremental variance of normalized even order harmonic power", IEEE Transactions on Power Delivery, vol. 6, no. 2, pp. 557-564, 1991.
[CrossRef] [SCOPUS Times Cited 53]


[10] B. D. Russell, R. P. Chinchali, and C. J. Kim, "Behavior of low frequency current components performance evalution using recorders field data", IEEE Transactions on Power Delivery, vol. 3, no. 4, pp. 1485-1492, 1988.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 47]


[11] B. D. Russell, K. Mehta, and R. P. Chinchali, "An arcing fault detection technique using low frequency current components performance evalution using recorders field data", IEEE Transactions on Power Delivery, vol. 3, no. 4, pp. 1493-1500, 1988.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 57]


[12] R. Christie, H. Zadhgole, and M. Habib, "High impedance fault detection in low voltage networks", IEEE Transactions on Power Delivery, vol. 8, no. 4, pp. 1829-1836, 1993.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 16]


[13] F. Ruz, and J. A. Fuentes, "Fuzzy decision making applied to high impedance fault detection in compensated neutral grounded MV distribution systems", in Proc. of 2001 IEE Conf. on Developments in Power System Protection, pp. 307-310, 2001.
[CrossRef]


[14] F. G. Jota, and P. R. S. Jota, "High-impedance fault identification using a fuzzy reasoning system", IEE Proc.-Gener.Transm.Distrib, vol. 145, no. 6, pp. 656-662, 1998.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 30]


[15] Jae-Ho. KO, Jae-Chul. Shim, Chang-Wan Ryu, Chan-Gook Park, and Wha-Yeong Yim, "Detection of high impedance fault using neural nets and chaotic degree", in Proc. of 1998 IEEE Energy Management and Power Deliver, vol. 2, pp. 399-404, 1998.
[CrossRef]


[16] R. Keyhani, M. Deriche, and E. Palmer, "A high impedance fault detector using a neural network and subband decomposition", in Proc. of 2001 IEEE Conference On Signal Processing and Its Applications, pp. 458-461, 2001.
[CrossRef] [SCOPUS Times Cited 14]


[17] Chul-Hwan Kim, Hyun Kim, Young-Hun Ko, Sung-Hyun Byun, Raj K. Aggarwal, and Allan T. Johns, "A novel fault-detection technique of high-impedance arcing faults in transmission lines using the wavelet transform", IEEE Transactions on Power Delivery, vol. 17, no. 4, pp. 921-929, 2002.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 127]


[18] T. M. Lai, L. A. Snider, and E. Lo. D. Sutanto, "High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion", IEEE Transactions on Power Delivery, vol. 20, no. 1, pp. 397-407, 2005.
[CrossRef] [Web of Science Times Cited 79] [SCOPUS Times Cited 109]


[19] M. Michalik, M. Lukowicz, W. Rebizant, S-J. Lee, S-H. Kang, "Verification of the wavelet-based HIF detecting algorithm performance in solidly grounded MV networks", IEEE Transactions on Power Delivery, vol. 22, no. 4, pp. 2057-2064, 2007.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 26]


[20] M. Michalik, W. Rebizant, M. Lukowicz, S. J. Lee, and S. H. Hang, "High impedance fault detection in distribution networks with use of wavelet based algorithm", IEEE Transactions on Power Delivery, vol. 21, no. 4 , pp. 1793-1802, 2006.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 87]


[21] S. A. Saleh, "A Wavelet Packet Transform-Based Differential Protection of Three-Phase Power Transformers," Master's Thesis, Memorial Univ. Newfoundland, St. John's, NF, Canada, 2003.

[22] E. Y. Hamid and Z. I. Kawasaki, "Wavelet-based data compression for power disturbances using minimum description length data," IEEE Transactions on Power Delivery, vol. 17, no. 2, pp. 460-466, Apr. 2002.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 138]


[23] Eduardo D. Sontag, "Feedback Stabilization Using Two-Bidden-Layer Nets," IEEE Trans. Neur. Networks, vol. 3 no. 6, pp. 981-990, 1992.
[CrossRef] [PubMed] [Web of Science Times Cited 93] [SCOPUS Times Cited 104]


References Weight

Web of Science® Citations for all references: 791 TCR
SCOPUS® Citations for all references: 1,157 TCR

Web of Science® Average Citations per reference: 34 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 2018-10-13 09:05 in 152 seconds.




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


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