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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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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.

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  4/2017 - 5

Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis

CHEN, R. See more information about CHEN, R. on SCOPUS See more information about CHEN, R. on IEEExplore See more information about CHEN, R. on Web of Science, SUN, X. See more information about  SUN, X. on SCOPUS See more information about  SUN, X. on SCOPUS See more information about SUN, X. on Web of Science, LIU, G. See more information about LIU, G. on SCOPUS See more information about LIU, G. on SCOPUS See more information about LIU, G. on Web of Science
 
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Download PDF pdficon (1,213 KB) | Citation | Downloads: 137 | Views: 99

Author keywords
power systems, fault location, phasor measurement units, kernel, principal component analysis

References keywords
power(17), systems(10), analysis(10), detection(9), system(7), component(7), fault(6), principal(5), kernel(4), components(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 37 - 42
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04005
Web of Science Accession Number: 000417674300005
SCOPUS ID: 85035786014

Abstract
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In this paper, based on Kernel Principal Component Analysis (KPCA) of Phasor Measurement Units (PMU) data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA) based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.


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

[1] X. Liu, D. M. Laverty, R. J. Best, K. Li, D. J. Morrow, S. McLoone, "Principal component analysis of wide-area phasor measurements for islanding detection - a geometric view," IEEE Transactions on Power Delivery, vol. 30, no. 2, pp. 976-985, 2015.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 16]


[2] W. J. Liu, Z. Z. Lin, F. S. Wen, G. Ledwich, "A wide area monitoring system based load restoration method," IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 2025-2034, 2013.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 28]


[3] Z. Zhao, C. Wang, Y. G. Zhang, Y. Sun, "Latest progress of fault detection and localization in complex electrical engineering," Journal of Electrical Engineering, vol. 65, no. 1, pp. 55-59, 2014.
[CrossRef] [Web of Science Times Cited 6]


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[CrossRef] [SCOPUS Times Cited 23]


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[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[6] S. Abraham, H. Dhaliwal, R. J. Efford, L. J. Keen, A. McLellan, J. Manley, K. Vollman, N. J. Diaz, T. Ridge et al., Final report on the August 14, 2003 blackout in the United states and Canada: causes and recommendations. US-Canada Power System Outage Task Force, 2004

[7] Y. Wang, W. Y. Li, J. P. Lu, "Reliability analysis of phasor measurement unit using hierarchical markov modeling," Electric Power Components and Systems, vol. 37, no. 5, pp. 517-532, 2009.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 38]


[8] R. Sodhi, S. C. Srivastava, S. N. Singh, "Phasor-assisted hybrid state estimator," Electric Power Components and Systems, vol. 38, no. 5, pp. 533-544, 2010.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 16]


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[CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 72]


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[CrossRef] [SCOPUS Times Cited 5]


[12] Y. G. Zhang, Z. P. Wang, J. F. Zhang, "A novel fault identification using WAMS/PMU," Advances in Electrical and Computer Engineering, vol. 12, no. 2, pp. 21-26, 2012.
[CrossRef] [Full Text] [Web of Science Times Cited 8] [SCOPUS Times Cited 9]


[13] E. Barocio, B. C. Pal, D. Fabozzi, N. F. Thornhill, "Detection and visualization of power system disturbances using principal component analysis," in Proc. IREP, 2013, pp. 1-10.
[CrossRef] [SCOPUS Times Cited 15]


[14] X. Liu, J. M. Kennedy, D. M. Laverty, D. John Morrow, Sean McLoone, "Wide area phase angle measurements for islanding detection - an adaptive nonlinear approach", IEEE Transactions on Power Delivery, vol. 31, no. 4, pp. 1901-1911, 2016.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[15] Le. Xie, Yang. Chen, P. R. Kumar, "Dimensionality reduction of synchrophasor data for early event detection: linearized analysis," IEEE Transactions on Power System, vol. 29, no. 6, pp.2784-2794, 2014.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 36]


[16] M. Ariff, B. C. Pal, "Coherency identification in interconnected power systems - an independent component analysis approach", IEEE Transactions on Power System, vol. 20, no. 2, pp. 1747-1755, 2013.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 44]


[17] Ali. Ajami, Mahdi. Daneshvar, "Data driven approach for fault detection and diagnosis of turbine in thermal power plant using independent component analysis," Electrical Power and Energy Systems, vol. 43, no. 1, pp.728-735, 2012.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 43]


[18] Jong. Min. Lee, Chang. Kyoo, Yoo, Sang. Wook Choi, Peter. A. Vanrolleghem, In. Beum. Lee, "Nonlinear process monitoring using kernel principal component analysis", Chemical Engineering Science, vol. 59, no. 1, pp. 223-234, 2004.
[CrossRef] [Web of Science Times Cited 364] [SCOPUS Times Cited 540]


[19] Shujie. Hou, Robert. Caiming. Qiu, "Kernel feature template matching for spectrum sensing," IEEE Transactions on Vehicular technology, vol. 63, no. 5, pp. 2258-2271, 2014.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 12]


[20] Bernhard Schölkopf, Alexander Smola, Klaus-Robert Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998.
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[22] F. Jia, E. B. Martin, A. J. Morris, "Non-linear principal components analysis with application to process fault detection," International Journal of Systems Science, vol. 31, no. 11, pp. 1473-1487, 2001.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 65]


[23] J. H. Hu,S. S. Xie,G. Q. Luo,Yang Fan,J. B. Peng, "Fault identification method of kernel principal component analysis based on contribution plots and its application," Systems Engineering and Electronics, vol. 30, no. 3, pp. 572-576, 2008



References Weight

Web of Science® Citations for all references: 4,104 TCR
SCOPUS® Citations for all references: 5,598 TCR

Web of Science® Average Citations per reference: 171 ACR
SCOPUS® Average Citations per reference: 233 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 2018-01-22 17:38 in 136 seconds.




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