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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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  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: 917 | Views: 2,379

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 81] [SCOPUS Times Cited 92]


[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 57] [SCOPUS Times Cited 77]


[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 8]


[4] D. Novosel, K. Vu, V. Centeno, S. Skok, M. Begovic, "Benefits of synchronized-measurement technology for power-grid applications", in Proc. 4th Annu. Hawaii Int. Conf. System Sciences, Hawaii, 2007, pp. 118-118.
[CrossRef] [SCOPUS Times Cited 32]


[5] M. Rarrerty, X. Liu, D. Laverty, S. McLoone, "Real-time multiple event detection and classification using moving window pca," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2537-2548, 2015.
[CrossRef] [Web of Science Times Cited 109] [SCOPUS Times Cited 134]


[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 45] [SCOPUS Times Cited 58]


[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 25] [SCOPUS Times Cited 31]


[9] R. Sodhi, S. C. Srivastava, S. N. Singh, "Optimal pmu placement method for complete topological and numerical observability of power system," Electric Power Components and Systems, vol. 80, no. 9, pp. 1154-1159, 2010.
[CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 103]


[10] C. H. Peng, H. J. Sun and J. F. Guo, "Multi-objective optimal pmu placement using a non-dominated sorting differential evolution algorithm," International Journal of Electrical Power & Energy Systems, vol. 32, no. 8, pp. 886-892, 2010.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 136]


[11] Arturo. R. Messina, N. I.. Moreno, J. J. Nuno, "Monitoring the health of large interconnected power systems: a near real-time perspective," in Proc. 8th Annu. IFAC Symposium on Fault Detection, Supervision and Safety of Tehnical Processes(SAFEPROCESS), Mexico, 2012, pp. 2-12.
[CrossRef] [SCOPUS Times Cited 4]


[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 10] [SCOPUS Times Cited 11]


[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 36]


[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 32] [SCOPUS Times Cited 38]


[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 172] [SCOPUS Times Cited 214]


[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 120] [SCOPUS Times Cited 144]


[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 84] [SCOPUS Times Cited 117]


[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 813] [SCOPUS Times Cited 1067]


[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 20] [SCOPUS Times Cited 23]


[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.
[CrossRef] [Web of Science Times Cited 5221] [SCOPUS Times Cited 6648]


[21] A. Rakotomamonjy, "Variable selection using svm based criteria," Journal of Machine Learning Research, vol. 3, no. 3, pp. 1357-1370, 2003

[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 61] [SCOPUS Times Cited 83]


[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: 7,032 TCR
SCOPUS® Citations for all references: 9,048 TCR

Web of Science® Average Citations per reference: 293 ACR
SCOPUS® Average Citations per reference: 377 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 2024-05-20 08:42 in 135 seconds.




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