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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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 HIGH-IMPACT PAPER 

Recent Research Progress in Fault Analysis of Complex Electric Power Systems

WANG, Z. See more information about WANG, Z. on SCOPUS See more information about WANG, Z. on IEEExplore See more information about WANG, Z. on Web of Science, ZHANG, Y. See more information about  ZHANG, Y. on SCOPUS See more information about  ZHANG, Y. on SCOPUS See more information about ZHANG, Y. 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 (688 KB) | Citation | Downloads: 1,991 | Views: 2,887

Author keywords
complexity, graph theory, multivariate statistical analysis theory, fault analysis, electric power system

References keywords
power(10), analysis(7), electric(6), cluster(6), system(5), systems(4), dynamics(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): 28 - 33
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01005
Web of Science Accession Number: 000275458900005
SCOPUS ID: 77954676685

Abstract
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In this paper, we adopt a novel approach to the fault analysis of complex electric power systems. Electric power system is one of the most complex artificial systems in the world. Its safe, steady, economical and reliable operating plays a very important part in guaranteeing socioeconomic development, and even in safeguarding social stability. The complexity of electric power system is determined by its characteristics about constitution, configuration, operation, organization, etc. No matter if, we adopt new analytical methods or technical means, we must have a distinct recognition of electric power system itself and its complexity, and increase analysis continuously, operation and control level. In this paper, utilizing real-time measurements of phasor measurement unit, based on graph theory and multivariate statistical analysis theory, we are using mainly Breadth-first search, Depth-first search and cluster analysis. Then, we seek for the uniform laws of marked changes of electrical quantities. Then we can carry out fast and exact analysis of fault component. Finally, we can accomplish fault isolation. According to line fault and bus-bar fault (single-phase fault, phase-to-phase fault and three-phase fault) in complex electric power systems, we have carried out a great deal of simulation experiments and obtained ideal results. These researches have proven that the faults in complex electric power systems can be explored successfully by analysis and calculation based on graph theory and multivariate statistical analysis theory.


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, 2003

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

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


[5] 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, 2007
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 18]


[6] 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, 2008
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 14]


[7] Y. G. Zhang, Y. Xu and Z. P. Wang, "Dynamical randomicity and predictive analysis in cubic chaotic system", Nonlinear Dynamics, doi:10.1007/s11071-009-9645-2, 2010
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 12]


[8] Y.G. Zhang, Y. Xu and Z.P. Wang, "GM(1,1) grey prediction of Lorenz chaotic system", Chaos, Solitons and Fractals, vol. 42. pp. 1003-1009, 2009
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 39]


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

[10] 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, 2007
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 61]


[11] 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, 2005
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 21]


[12] 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, 2006
[CrossRef] [Web of Science Times Cited 185] [SCOPUS Times Cited 251]


[13] A. Z. Arifin and A. Asano, "Image segmentation by histogram thresholding using hierarchical cluster analysis", Pattern Recognition Letters, vol. 27, pp.1515-1521, 2006
[CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 184]


[14] X. Otazu and O. Pujol, "Wavelet based approach to cluster analysis. Application on low dimensional data sets", Pattern Recognition Letters, vol. 27, pp.1590-1605, 2006
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6]


[15] H. S. Park and D. K. Baik, "A study for control of client value using cluster analysis", Journal of Network and Computer Applications, vol.29, pp. 262-276, 2006
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]


[16] V. Tola, F. Lillo, M. Gallegati and R.N. Mantegna, "Cluster analysis for portfolio optimization", Journal of Economic Dynamics and Control, vol.32, pp.235-258, 2008
[CrossRef] [Web of Science Times Cited 143] [SCOPUS Times Cited 177]


[17] W. X. Zhao, P. K. Hopke and K. A. Prather, "Comparison of two cluster analysis methods using single particle mass spectra", Atmospheric Environment, vol. 42, pp. 881-892, 2008
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 31]


[18] M. Templ, P. Filzmoser and C. Reimann, "Cluster analysis applied to regional geochemical data: Problems and possibilities", Applied Geochemistry, vol. 23, pp. 2198-2213, 2008
[CrossRef] [Web of Science Times Cited 279] [SCOPUS Times Cited 309]


[19] J. A. Bondy and U. S. R. Murth, "Graph Theory with Applications", Elsevier Science Publishing Co., Inc., New York, 1976 [PermaLink]

[20] D. E. Knuth, "The Art of Computer Programming", Third Edition, Addison-Wesley, Boston, 1997 [PermaLink]

[21] H. C. Thomas, E. L. Charles, L. R. Ronald and S. Clifford, "Introduction to Algorithms", Second Edition, MIT Press and McGraw-Hill, Cambridge, 2001 [PermaLink]

References Weight

Web of Science® Citations for all references: 908 TCR
SCOPUS® Citations for all references: 1,153 TCR

Web of Science® Average Citations per reference: 43 ACR
SCOPUS® Average Citations per reference: 55 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 2024-06-11 08:41 in 96 seconds.




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Stefan cel Mare University of Suceava, Romania


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