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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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2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

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SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

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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: 1,281 | Views: 4,702

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
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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 153] [SCOPUS Times Cited 243]


[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 59] [SCOPUS Times Cited 73]


[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 38]


[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 108] [SCOPUS Times Cited 141]


[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 39] [SCOPUS Times Cited 59]


[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 53] [SCOPUS Times Cited 85]


[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 29] [SCOPUS Times Cited 36]


[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 76] [SCOPUS Times Cited 91]


[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 20] [SCOPUS Times Cited 19]


[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 10] [SCOPUS Times Cited 15]


[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: 613 TCR
SCOPUS® Citations for all references: 841 TCR

Web of Science® Average Citations per reference: 32 ACR
SCOPUS® Average Citations per reference: 44 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 2022-10-04 12:25 in 83 seconds.




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