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A Differential Particle Swarm Optimization-based Support Vector Machine Classifier for Fault Diagnosis in Power Distribution SystemsCHO, M. Y. , HOANG, T. T.
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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)
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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 12] [SCOPUS Times Cited 17]
 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 60] [SCOPUS Times Cited 80]
 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 310] [SCOPUS Times Cited 494]
 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 75] [SCOPUS Times Cited 103]
 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 64] [SCOPUS Times Cited 86]
 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 19] [SCOPUS Times Cited 36]
 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 25] [SCOPUS Times Cited 29]
 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 7] [SCOPUS Times Cited 11]
 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 10]
 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 24] [SCOPUS Times Cited 28]
 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 36] [SCOPUS Times Cited 40]
 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 218] [SCOPUS Times Cited 297]
 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 268] [SCOPUS Times Cited 347]
 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 52] [SCOPUS Times Cited 63]
 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 99] [SCOPUS Times Cited 136]
 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 9] [SCOPUS Times Cited 9]
 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 4643] [SCOPUS Times Cited 6119]
 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 64] [SCOPUS Times Cited 73]
 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 1086] [SCOPUS Times Cited 1429]
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