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JCR Impact Factor: 1.102
JCR 5-Year IF: 0.734
Issues per year: 4
Current issue: Feb 2021
Next issue: May 2021
Avg review time: 57 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: 643243560
doi: 10.4316/AECE


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FEATURED ARTICLE

Improved Wind Speed Prediction Using Empirical Mode Decomposition, ZHANG, Y., ZHANG, C., SUN, J., GUO, J.
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2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

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2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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  2/2012 - 4

 HIGH-IMPACT PAPER 

A Novel Fault Identification Using WAMS/PMU

ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on IEEExplore See more information about ZHANG, Y. on Web of Science, WANG, Z. See more information about  WANG, Z. on SCOPUS See more information about  WANG, Z. on SCOPUS See more information about WANG, Z. on Web of Science, ZHANG, J. See more information about ZHANG, J. on SCOPUS See more information about ZHANG, J. on SCOPUS See more information about ZHANG, J. on Web of Science
 
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Download PDF pdficon (761 KB) | Citation | Downloads: 1,054 | Views: 4,208

Author keywords
fault identification, noise, principal component analysis, wide area measurement system, wams

References keywords
power(12), systems(11), analysis(8), fault(7), electric(6), research(5), principal(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-05-30
Volume 12, Issue 2, Year 2012, On page(s): 21 - 26
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.02004
Web of Science Accession Number: 000305608000004
SCOPUS ID: 84865279517

Abstract
Quick view
Full text preview
The important premise of the novel adaptive backup protection based on wide area information is to identify the fault in a real-time and on-line way. In this paper, the principal components analysis theory is introduced into the field of fault detection to locate precisely the fault by mean of the voltage and current phasor data from the PMUs. Massive simulation experiments have fully proven that the fault identification can be performed successfully by principal component analysis and calculation. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Under the influence of noise, the results are still accurate and reliable. So, the principal components fault identification has strong anti-interference ability and great redundancy.


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

[1] A.G. Phadke and J.S. Thorp, "Expose hidden failures to prevent cascading outages," IEEE Computer Applications in Power, vol.9, pp. 20-23, Jul. 1996
[CrossRef] [Web of Science Times Cited 142] [SCOPUS Times Cited 232]


[2] A.G. Phadke and J.S. Thorp, "Synchronized phasor measurements and their applications", Springer Verlag, 2008.

[3] Y. G. Zhang, Z. P. Wang, J. F. Zhang and J. Ma, "Fault localization in electrical power systems: A pattern recognition approach," International Journal of Electric Power & Energy Systems, vol.33, pp.791-798, Mar. 2011
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 70]


[4] C. Wang, Q. Q. Jia, X. B. Li and C. X. Dou, "Fault location using synchronized sequence measurements," International Journal of Electrical Power & Energy Systems, vol.30. pp. 134-139, Feb.2008
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 37]


[5] T. S. Bi, X. H. Qin and Q. X. Yang, "A novel hybrid state estimator for including synchronized phasor measurements," Electric Power Systems Research, vol.78, pp. 1343-1352, Aug. 2008
[CrossRef] [Web of Science Times Cited 98] [SCOPUS Times Cited 131]


[6] C. Wang, C. X. Dou, X. B. Li and Q. Q. Jia, "A WAMS/PMU-based fault location technique," Electric Power Systems Research, vol. 77, pp. 936-945, Jun. 2007
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 55]


[7] Z. P. Wang, Y. G. Zhang and J. F. Zhang, "Recent research progress in fault analysis of complex electric power systems," Advances in Electrical and Computer Engineering, vol.10, pp.28-33, Feb. 2010
[CrossRef] [Full Text] [Web of Science Times Cited 18] [SCOPUS Times Cited 22]


[8] L. X. Dong, D. M. Xiao, Y. S. Liang and Y. L. Liu, "Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers," Electric Power Systems Research, vol.78, pp. 129-136, Jan. 2008
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 78]


[9] Y. G. Zhang, Z. P. Wang, J. F. Zhang and J. Ma, "PCA fault feature extraction in complex electric power systems," Advances in Electrical and Computer Engineering, vol.10, pp.102-107, Aug. 2010
[CrossRef] [Full Text] [Web of Science Times Cited 15] [SCOPUS Times Cited 19]


[10] P. Giordania and H. Kiersb, "Principal component analysis of symmetric fuzzy data," Computational Statistics & Data Analysis, vol.45, pp. 519-548, Apr. 2004
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 33]


[11] P. L. Cui, J. H. Li and G. Z. Wang, "Improved kernel principal component analysis for fault detection," Expert Systems with Applications, vol.34, pp. 1210-1219, Feb. 2008
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 82]


[12] C. D. Lu, C. M Zhang, T. Y. Zhang and W. Zhang, "Kernel based symmetrical principal component analysis for face classification," Neurocomputing, vol.70, pp. 904-911, Jan. 2007
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 18]


[13] M.A. Perry, H.P. Wynn and R.A. Bates, "Principal components analysis in sensitivity studies of dynamic systems," Probabilistic Engineering Mechanics, vol. 21, pp. 454-460, Oct. 2006
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 14]


[14] A. G. Phadke and J. S. Thorp, Computer relaying for power system, Second edition, John Wiley & Sons Ltd, Chichester, 2009.

[15] R. Johnson and D. Wichern, Applied multivariate statistical analysis, Prentice Hall, London, 2002.

[16] J. P. Zhu, Applied multivariate statistical analysis, Science Press, Beijing, 2006.

[17] D. Johnson, Applied multivariate methods for data analysts, Duxbury Press, Pacific Grove, CA, 1998.

[18] IEEE Std C37.118TM-2005, IEEE standard for synchrophasors for power systems, IEEE, New York, 2006.



References Weight

Web of Science® Citations for all references: 568 TCR
SCOPUS® Citations for all references: 791 TCR

Web of Science® Average Citations per reference: 30 ACR
SCOPUS® Average Citations per reference: 42 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 2021-03-04 00:15 in 75 seconds.




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


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