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Fault Localization for Synchrophasor Data using Kernel Principal Component AnalysisCHEN, R. , SUN, X. , LIU, G.
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power systems, fault location, phasor measurement units, kernel, principal component analysis
power(17), systems(10), analysis(10), detection(9), system(7), component(7), fault(6), principal(5), kernel(4), components(4)
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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
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|
| 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 30] [SCOPUS Times Cited 33]
 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 23] [SCOPUS Times Cited 33]
 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 7]
 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 25]
 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 16] [SCOPUS Times Cited 20]
 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
 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 35] [SCOPUS Times Cited 47]
 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 18] [SCOPUS Times Cited 22]
 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 45] [SCOPUS Times Cited 65]
 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 61] [SCOPUS Times Cited 79]
 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 5]
 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 9] [SCOPUS Times Cited 10]
 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 17]
 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 10] [SCOPUS Times Cited 10]
 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 50] [SCOPUS Times Cited 65]
 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 58] [SCOPUS Times Cited 74]
 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 38] [SCOPUS Times Cited 50]
 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 443] [SCOPUS Times Cited 608]
 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 10] [SCOPUS Times Cited 16]
 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 3701] [SCOPUS Times Cited 4897]
 A. Rakotomamonjy, "Variable selection using svm based criteria," Journal of Machine Learning Research, vol. 3, no. 3, pp. 1357-1370, 2003
 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 47] [SCOPUS Times Cited 68]
 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
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