|4/2017 - 5|
Fault Localization for Synchrophasor Data using Kernel Principal Component AnalysisCHEN, R. , SUN, X. , LIU, G.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,213 KB) | Citation | Downloads: 435 | Views: 1,271|
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)
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
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 41] [SCOPUS Times Cited 49]
 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 35] [SCOPUS Times Cited 48]
 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 26]
 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 45] [SCOPUS Times Cited 48]
 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 40] [SCOPUS Times Cited 50]
 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 20] [SCOPUS Times Cited 25]
 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 57] [SCOPUS Times Cited 79]
 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 76] [SCOPUS Times Cited 103]
 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 10] [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 21]
 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 17] [SCOPUS Times Cited 19]
 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 75] [SCOPUS Times Cited 98]
 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 78] [SCOPUS Times Cited 103]
 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 45] [SCOPUS Times Cited 58]
 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 543] [SCOPUS Times Cited 724]
 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 15] [SCOPUS Times Cited 19]
 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 4147] [SCOPUS Times Cited 5318]
 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 52] [SCOPUS Times Cited 72]
 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
Web of Science® Citations for all references: 5,303 TCR
SCOPUS® Citations for all references: 6,875 TCR
Web of Science® Average Citations per reference: 221 ACR
SCOPUS® Average Citations per reference: 286 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 2020-06-01 21:40 in 139 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.