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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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  3/2010 - 17

 HIGH-IMPACT PAPER 

PCA Fault Feature Extraction in Complex Electric Power Systems

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, MA, J. See more information about MA, J. on SCOPUS See more information about MA, J. on SCOPUS See more information about MA, J. on Web of Science
 
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Download PDF pdficon (575 KB) | Citation | Downloads: 1,613 | Views: 5,759

Author keywords
complexity, fault feature extraction, principal components analysis, PCA, phasor measurement unit, PMU, electric power system

References keywords
power(11), electric(7), analysis(6), systems(5), system(5)
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): 102 - 107
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.03017
Web of Science Accession Number: 000281805600017
SCOPUS ID: 77956623447

Abstract
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Electric power system is one of the most complex artificial systems in the world. The complexity is determined by its characteristics about constitution, configuration, operation, organization, etc. The fault in electric power system cannot be completely avoided. When electric power system operates from normal state to failure or abnormal, its electric quantities (current, voltage and angles, etc.) may change significantly. Our researches indicate that the variable with the biggest coefficient in principal component usually corresponds to the fault. Therefore, utilizing real-time measurements of phasor measurement unit, based on principal components analysis technology, we have extracted successfully the distinct features of fault component. Of course, because of the complexity of different types of faults in electric power system, there still exists enormous problems need a close and intensive study.


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

[1] J. X. Yuan, "Wide area protection and emergency control to prevent large scale blackout", China Electric Power Press, Beijing, 2007.

[2] L. Ye, "Study on sustainable development strategy of electric power in China in 2020", Electric Power, vol. 36, pp. 1-7, Oct. 2003.

[3] Y. S. Xue, "Interactions between power market stability and power system stability", Automation of Electric Power Systems, vol. 26, pp. 1-6, Nov. 2002.

[4] M. J. Baxter, "Standardization and transformation in principal components analysis, with applications to archaeometry", Applied Statistics, vol. 44, pp. 513-527, Oct. 1995.
[CrossRef] [Web of Science Times Cited 108]


[5] M. D. Glascock, E. B. Geoffrey and H. C. Robert, A systematic approach to obsidian source characterization, Plenum Publishing Co., New York, 1998.

[6] X. L. Yu and X. S. Ren, Multivariate statistical analysis, China Statistic Press, 1998.

[7] Y. G. Zhang, C. J. Wang and Z. Zhou, "Inherent randomicity in 4-symbolic dynamics", Chaos, Solitons and Fractals, vol. 28, pp. 236-243, Apr. 2006.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 19]


[8] Y. G. Zhang and C. J. Wang, "Multiformity of inherent randomicity and visitation density in n-symbolic dynamics", Chaos, Solitons and Fractals, vol. 33, pp. 685-694, Jul. 2007.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 18]


[9] Y. G. Zhang and Z. P. Wang, "Knot theory based on the minimal braid in Lorenz system", International Journal of Theoretical Physics, vol. 47, pp. 873-880, Apr. 2008.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 14]


[10] Y. G. Zhang, Y. Xu and Z.P. Wang, "Dynamical randomicity and predictive analysis in cubic chaotic system", Nonlinear Dynamics, vol. 61, pp. 241-249, Jul. 2010.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 12]


[11] Y. G. Zhang, J. F. Zhang, Q. Ma, J. Ma and Z. P. Wang, "Statistical description and forecasting analysis of life system", International Journal of Nonlinear Sciences and Numerical Simulation, vol.11, pp. 157-163, Mar. 2010.

[12] Z. P. Wang, Y. G. Zhang, J. F. Zhang and J. Ma, "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 19] [SCOPUS Times Cited 23]


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

[14] 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 41] [SCOPUS Times Cited 61]


[15] C. Rakpenthai, S. Premrudeepreechacharn, S. Uatrongjit and N.R. Watson, "Measurement placement for power system state estimation using decomposition technique", Electric Power Systems Research, vol. 75, pp.41-49, Jul. 2005.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 21]


[16] J. N. Peng, Y. Z. Sun and H. F. Wang, "Optimal PMU placement for full network observability using Tabu search algorithm", International Journal of Electrical Power & Energy Systems, vol. 28, pp. 223-231, May. 2006.
[CrossRef] [Web of Science Times Cited 184] [SCOPUS Times Cited 250]


[17] M. D. Glascock, Characterization of archaeological ceramics at MURR by neutron activation analysis and multivariate statistics, Prehistory Press, Madison, 1992.

[18] J. E. Jackson, A user's guide to principal components, John Wiley and Sons, New York, 1991.
[CrossRef]


References Weight

Web of Science® Citations for all references: 418 TCR
SCOPUS® Citations for all references: 418 TCR

Web of Science® Average Citations per reference: 23 ACR
SCOPUS® Average Citations per reference: 23 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 2024-04-20 14:40 in 60 seconds.




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