|3/2017 - 7|
A Differential Particle Swarm Optimization-based Support Vector Machine Classifier for Fault Diagnosis in Power Distribution SystemsCHO, M. Y. , HOANG, T. T.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,322 KB) | Citation | Downloads: 280 | Views: 636|
fault diagnosis, particle swarm optimization, power distribution lines, reflectometry, support vector machines
power(16), fault(15), systems(13), location(9), distribution(9), system(7), networks(7), artificial(6), swarm(5), neural(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 51 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03007
Web of Science Accession Number: 000410369500007
SCOPUS ID: 85028567448
This paper proposes a new differential particle swarm optimization (DPSO) method for obtaining optimum support vector machine (SVM) parameters used for electrical fault diagnosis in radial distribution systems. Further, a multiple-stage DPSO-SVM classifier is developed to enhance classification accuracy in the fault diagnosis. Also, time-domain reflectometry (TDR) method with pseudo-random binary sequence (PRBS) excitation is utilized for generating the dataset required for validating this proposed approach. According to the characteristic of echo responses found in different types of faults, 12 features are extracted as input vectors for purposes of classification. The proposed fault diagnosis approach is tested on a typical radial distribution system to classify ten types of short-circuit faults accurately. Further, to demonstrate the superiority of the proposed DPSO algorithm, comparative studies of fault diagnosis are performed using SVM having parameters selected using cross-validation, GA and PSO. The overall classification accuracy obtained for fault diagnosis is 98.5%, which shows the effectiveness of the proposed approach.
|References|||||Cited By «-- Click to see who has cited this paper|
| J. G. M. S. Decanini, M. S. Tonelli-Neto, C. R. Minussi, "Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory," IET Gener. Transm. Distrib, vol. 6, pp. 1112-1120, Jul. 2012. |
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 14]
 E. C. Senger, G. Manassero, C. Goldemberg, E. L. Pellini, "Automated fault location system for primary distribution networks," IEEE Trans. Power Delivery, vol. 20, pp. 1332-1340, 2005.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 77]
 Y. Liao, "Algorithms for power system fault location and line parameter estimation," 39th Southeastern Symposium on System Theory, Mercer University, Macon, GA, 2007.
[CrossRef] [SCOPUS Times Cited 18]
 F. H. Magnago, A. Abur, "Fault location using wavelets," IEEE Trans. Power Delivery, vol. 13, pp. 1475-1480, 1998.
[CrossRef] [Web of Science Times Cited 279] [SCOPUS Times Cited 467]
 A. Borgheti, S. Corsi, C. A. Nucci, M. Paolone, L. Pereto, R. Tinarelli, "On the use of continuous-wavelet transform for fault location in distribution power systems," Electrical Power and Energy Systems, vol. 28, pp. 608-617, 2006.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 93]
 M. N. Pourahmadi, A. A. Safavi. "Path characteristic frequency-based fault locating in radial distribution systems using wavelets and neuron networks," IEEE Trans. Power Delivery, vol. 60, pp. 1654-1663, 2011.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 78]
 M. Al. Shaher, M. M. Sabra, A. S. Saleh, "Fault location in multi-ring distribution networks using artificial neural network," Electric Power Systems Research, vol. 64, pp. 87-92, 2003.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 34]
 J. P. Steiner, W. L. Weeks, H. W. Ng, "An automated fault locating system," IEEE Trans. on Power Delivery, vol. 7, pp. 967978, 1992.
[CrossRef] [Web of Science Record]
 S. Navaneethan, J. J. Soraghan, W. H. Siew, F. McPherson, P. F. Gale, "Automatic Fault Location for Underground Low Voltage Distribution Networks," IEEE Transactions on power delivery, Vol. 16, pp. 346-351, 2001.
 J. Mora-Flórez, J. Cormane-Angarita, G. Carrillo-Caicedo, "K-means algorithm and mixture distributions for locating faults in power systems," Electric Power System Research, vol. 79, pp. 714-721, 2009.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 26]
 H. Mokhlis, H. Mohamad, H. Li, Ab H. A. Bakar, "Voltage Sags Matching to Locate Faults for Underground Distribution Networks," Advances Electrical and Computer Engineering, vol. 11, No.2, pp. 43-48 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 6] [SCOPUS Times Cited 9]
 J. J. Mathew, A. Francis, "HVDC Transmission Line Fault Location Using Wavelet Feeded Neural Network Bank," Science Technology & Engineering, vol. 2, pp. 1-6, 2013.
[CrossRef] [SCOPUS Times Cited 5]
 X. Deng, R. Yuan, Z. Xiao, T. Li, K. L. L. Wanga, "Fault location in loop distribution network using SVM technology," Electrical Power and Energy Systems, vol. 65, pp. 254-261, 2015.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 20]
 X. Zhang, M. Zhang and D. Liu, "Reconstruction of faulty cable network using time domain reflectometry," Progress In Electromagnetics Research, vol. 136, pp. 457-478, 2013.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]
 L. Ye, D. You, X. Yin, K. Wang, J. Wu, "An improved fault-location method for distribution system using wavelets and support vector regression," Electrical Power and Energy Systems, vol. 55, pp. 467-472, 2014.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 35]
 V. N. Vapnik, "The Nature of Statistical Learning Theory," Springer. Verlag, New York, pp. 1-14, 1995.
 C. Hsu, C. Chang, C. Lin, "A practical guide to support vector classification," Department of Computer Science, National Taiwan University, Tech. Report, pp. 1- 16, 2003.
 L. B. Jack and A. K. Nandi, "Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms," Mechanical Systems and Signal Processing, vol. 16, pp. 373-390, 2002.
[CrossRef] [Web of Science Times Cited 203] [SCOPUS Times Cited 269]
 B. Samanta, K. R. Al-Balushi, and S. A. Al-Araimi, "Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection," Engineering Applications of Artificial Intelligence, vol. 16, pp. 657-665, 2003.
[CrossRef] [Web of Science Times Cited 242] [SCOPUS Times Cited 314]
 Z. C. Johanyák, and O. Papp, "A hybrid algorithm for parameter tuning in fuzzy model identification," Acta Polytechnica Hungarica, vol. 9, pp. 153-165, 2012.
 R. E. Precup, R. C. David, E. M. Petriu, S. Preitl, and M. B. Ra?dac, "Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers," Expert Systems with Applications, vol. 41, pp. 1168-1175, 2014.
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 58]
 I. P. Solos, I. X. Tassopoulos, and G. N. Beligiannis, "Optimizing shift scheduling for tank trucks using an effective stochastic variable neighbourhood approach," International Journal of Artificial Intelligence, vol. 14, pp. 1-26, 2016.
 M. S. Kirana, and O. Findik,"A directed artificial bee colony algorithm," Applied Soft Computing, vol. 26, pp. 454-462, 2015.
[CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 94]
 A. Basgumus, M. Namdar, G. Yilmaz, A. Altuncu, "Performance Comparison of the Differential Evolution and Particle Swarm Optimization Algorithms in Free-Space Optical Communications Systems," Advances Electrical and Computer Engineering, vol. 15, pp. 17-22, 2015.
[CrossRef] [Full Text] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]
 M. H. David, A. G. Richard, "A novel pulse echo correlation tool for transmission path testing and fault diagnosis," Journal of computers, vol. 11, pp. 31-39, 2006.
[CrossRef] [Web of Science Record]
 R. C. Eberhart, Y. Shi, J. Kennedy, "Swarm Intelligence," Morgan Kaufmann Publishers Inc, San Francisco, CA, 2001.
 R. Eberhart, Y. Shi, "Comparing inertia weights and constriction factors in particle swarm optimization," in Proceedings Congress on in Evolutionary Computation, vol. 1, pp. 84-88, 2000.
 M. Clerc, J. Kennedy, "The particle swarm - explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, vol. 6, pp. 58-73, 2002.
[CrossRef] [Web of Science Times Cited 4148] [SCOPUS Times Cited 5584]
 M. N. Alam, B. Das, and V. Pant, "A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination," Electric Power Systems Research, vol. 128, pp. 39-52, 2015.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 42]
 Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, and R. Harley, "Particle swarm optimization: Basic concepts, variants and applications in power systems," IEEE Transactions on Evolutionary Computation, vol. 12, pp. 171-195, 2008.
[CrossRef] [Web of Science Times Cited 838] [SCOPUS Times Cited 1291]
Web of Science® Citations for all references: 6,147 TCR
SCOPUS® Citations for all references: 8,541 TCR
Web of Science® Average Citations per reference: 198 ACR
SCOPUS® Average Citations per reference: 276 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 2018-12-11 07:24 in 182 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.