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Fault Location on Radial Distribution Systems Using Wavelets and Artificial Neural Networks with a New Data Processing FeatureNERI Jr., A. L.   , MOREIRA, F. A. , de SOUZA, B. A. |
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Author keywords
distribution power systems, fault location, wavelet transform, data preprocessing, artificial neural networks
References keywords
power(25), fault(22), distribution(21), systems(18), location(18), wavelet(14), neural(10), networks(9), analysis(7), wavelets(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2024-05-31
Volume 24, Issue 2, Year 2024, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.02001
Web of Science Accession Number: 001242091800001
SCOPUS ID: 85195640726
Abstract
Every power system is vulnerable to fault occurrences. Under permanent fault conditions, maintenance crew has the duty to detect the problem, repair the defect and recover the power supply system. If the fault is previously located, the repair can be performed faster. The common methods to locate the fault in electrical distribution systems use the final user information or some heuristics with fuse coordination and the loss of loads. In this paper, a new algorithm for fault location in distribution power systems is presented. Using computational simulations, travelling waves theory, wavelet transform, a new data preprocessing feature, and artificial neural networks, this new algorithm tries to approximate the fault location using data provided by only one measurement point at the beginning of the feeder. |
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| [1] IEEE Guide for determining fault location on AC transmission and distribution lines, IEEE Standard C37.114-2014, Jan. 2015. [CrossRef] [2] F. H. Magnago and A. Abur, "A new fault location technique for radial distribution systems based on high frequency signals," Proc. IEEE Power Engineering Society Summer Meeting, vol. 1, pp. 426-431, 1999. [CrossRef] [SCOPUS Times Cited 71] [3] D. Jalali, N. Moslemi, "Fault location for radial distribution systems using fault-generated high-frequency transients and wavelet analysis," Proc. International Conference and Exhibition on Electricity Distribution, 2005. [CrossRef] [4] F. Yan, Z. Chen, Z. Liang, Y. Kong, P. Li, "Fault location using wavelet packets," Proc. International Conference on Power Systems Technology, Powercon, vol. 4, pp. 2575-2579, 2002. [CrossRef] [SCOPUS Times Cited 27] [5] U. D. Dwivedi, S. N. Singh, S. C. Srivastava, "A wavelet based approach for classification and location of faults in distribution systems," Proc. IEEE Annual India Conference, vol. 2, pp. 488-493, 2008. [CrossRef] [6] B. A. Souza, K. M. Silva, H. D. M. Braz, J. A. C. B. Silva, A. V. Fontes, "Fault location for distribution systems based on decision rules and wavelet transform," Proc. International Conference and Exhibition on Electricity Distribution, 2005. [CrossRef] [7] F. Chunju, K. K. Li, W. L. Chan, Y. Weiyong, Z. Zhaoning, "Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines," International Journal of Electrical Power and Energy Systems, vol. 29, no. 6, pp. 497-503, 2007. [CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 82] [8] Y. Aslan, Y. E. Yagan, "Artificial neural-network-based fault location for power distribution lines using the frequency spectra of fault data," Electrical Engineering, vol. 99, pp. 301-311, 2017. [CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 38] [9] H. Nouri, C. Wang, T. Davies, "An accurate fault location technique for distribution lines with tapped load using wavelet transform," IEEE Porto PowerTech Conference, vol. 3, 2001. [CrossRef] [SCOPUS Times Cited 52] [10] N. I. Elkalahashy, M. Lehtonen, H. A. Darwish, A.-M. I. Taalab, M. A. Izzularab, "DWT-Based detection and transient power direction-based location of high impedance faults due to leaning trees in unearthed MV networks," IEEE Transactions on Power Delivery, vol. 23, no. 1, pp. 94-101, Jan 2008. [CrossRef] [Web of Science Times Cited 73] [SCOPUS Times Cited 99] [11] L. Peretto, R. Sasdelli, E. Scala, R. Tinarelli, "Fault location method integrating a distributed measurement system and wavelet analysis," Proc. Instrumentation and Measurement Technology Conference, pp. 1-6, May 2007. [CrossRef] [SCOPUS Times Cited 7] [12] M. Goudarzi, B. Vahdi, R. A. Naghizadeh, S. H. Hosseinian, "Improved fault location algorithm for radial distribution systems with discrete and continuous wavelet analysis," International Journal of Electrical Power and Energy Systems, vol. 67, pp. 423-430, 2015. [CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 80] [13] T. Jahromi, P. Wolfs, S. Islam, "Travelling wave fault location in rural radial distribution networks to reduce wild fire risk," Proc. Australasian Universities Power Engineering Conference, 2015. [CrossRef] [SCOPUS Times Cited 8] [14] A. P. Biscaro, R. A .F. Pereira, M. Kezunovic, J. R. S. Mantovani, "Integrated fault location and power quality analysis in electric power distribution systems," IEEE Transactions on Power Delivery, vol. 31, no. 2, pp. 428-436, Apr. 2016. [CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 76] [15] T. F. Moraes, L. Lovisolo, L. F. CS. Monteiro, "Fault location in distribution systems from analysis of the energy of sequence component waveforms," IET Generation, Transmission & Distribution, vol 12, no. 9, pp. 1951-1960, May 2018. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 10] [16] M. Y. Cho, T. T. Hoang, "A differential particle swarm optimization-based support vector machine classifier for fault diagnosis in power distribution systems," Advances in Electrical and Computer Engineering, vol.17, no. 3, pp.51-60, 2017. [CrossRef] [Full Text] [Web of Science Times Cited 5] [SCOPUS Times Cited 8] [17] S. Chattopadhyay, B. Roy, A. Bera, G. Humne, M. S. Alam and G. Bandyopadhyay, "Kurtosis-Skewness scanning and machine learning-based discrimination of fault location in radial power distribution network," Proc. International Conference on Power Electronics and Energy (ICPEE), Bhubaneswar, India, pp. 1-6, 2023. [CrossRef] [SCOPUS Times Cited 1] [18] A. Borghetti, S. Corsi, C. A. Nucci, M. Paolone, L. Peretto, R. Tinarelli, "On the use of continuous-wavelet transform for fault location in distribution power systems," International Journal of Electrical Power and Energy Systems, vol. 28, no. 9, pp. 608-617, Nov. 2006. [CrossRef] [Web of Science Times Cited 89] [SCOPUS Times Cited 129] [19] A. Borghetti, M. Bosetti, M. di Silvestro, C. A. Nucci, M. Paolone, "Continuous-wavelet transform for fault location in distribution power networks: Definition of mother wavelets inferred from fault originated transients," IEEE Transactions on Power Systems, vol. 23, no. 2, pp. 380-388, May 2008. [CrossRef] [Web of Science Times Cited 220] [SCOPUS Times Cited 286] [20] A. Borghetti, M. Bosetti, M. Paolone, A. Abur, "Integrated use of time-frequency wavelet decompositions for fault location in distribution networks: Theory and experimental validation," IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 3139-46, 2010. [CrossRef] [Web of Science Times Cited 164] [SCOPUS Times Cited 214] [21] M. Pourahmadi-Nakhli, A. A. Safavi, "Path characteristic frequency-based fault locating in radial distribution systems using wavelets and neural networks," IEEE Transactions on Power Delivery, vol. 26, no. 2, pp. 772-781, 2011. [CrossRef] [Web of Science Times Cited 111] [SCOPUS Times Cited 146] [22] L. Van der Sluis, Transients in Power Systems, John Wiley and Sons, pp. 31-56, 2001 [23] C. Gargour, M. Gabrea, V. Ramchandran, J.-M. Lina, "A short introduction to wavelets and their applications," IEEE Circuits and Systems Magazine, vol. 9, no. 2, pp. 57-68, 2009. [CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 83] [24] R. M. de C. Fernandez, H. N. D. Rojas, "An overview of wavelet transforms application in power systems," Proc. 14th Power System Computation Conference, Seville, Spain, 2002 [25] I. Daubechies, Ten lectures on wavelets, SIAM Publications Library, pp. 129-138, 1992 [26] S. Walker, A primer on wavelets and their scientific applications, CRC Press, pp. 41-85, 1999 [27] W. Galli, G. T. Heydt and P. F. Ribeiro, "Exploring the power of wavelet analysis," IEEE Computer Applications in Power, vol. 9, no. 4, pp. 37-41, Oct. 1996. [CrossRef] [Web of Science Times Cited 72] [SCOPUS Times Cited 111] [28] T. K. Sarkar, C. Su, "A tutorial on wavelets from an electrical engineering perspective, part 1: Discrete wavelet techniques," IEEE Antennas and Propagation Magazine, vol 40, no. 5, pp. 49-68, 1998. [CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 90] [29] S. Haykin, Neural Networks and Learning Machines, 3rd ed. Pearson Prentice Hall, pp. 122-166, 2009 [30] J. Sola and J. Sevilla, "Importance of input data normalization for the application of neural networks to complex industrial problems," IEEE Transactions on Nuclear Science, vol. 44, no. 3, pp. 1464-1468, June 1997. [CrossRef] [Web of Science Times Cited 545] [SCOPUS Times Cited 632] [31] S. I. Koval, "Data preparation for neural network data analysis," Proc. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow and St. Petersburg, Russia, pp. 898-901 2018. [CrossRef] [SCOPUS Times Cited 18] [32] J. Jin, M. Li, L. Jin, "Data normalization to accelerate training for linear neural net to predict tropical cyclone tracks," Mathematical Problems in Engineering, Article ID: 931629, 8 pages, 2015. [CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 71] [33] M. Kolarik, R. Burget and K. Riha, "Comparing normalization methods for limited batch size segmentation neural networks," Proc. International Conference on Telecommunications and Signal Processing (TSP), Milan, Italy, pp. 677-680, 2020. [CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 19] [34] E. Ogasawara, L. C. Martinez, D. de Oliveira, G. Zimbrão, G. L. Pappa and M. Mattoso, "Adaptive normalization: a novel data normalization approach for non-stationary time series," Proc. International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, pp. 1-8, 2010. [CrossRef] [Web of Science Times Cited 74] [SCOPUS Times Cited 123] [35] A. Kumar, "Effects of Sdifferent normalization techniques on the convolutional neural network," Proc. International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, pp. 201-204, 2021 Web of Science® Citations for all references: 1,766 TCR SCOPUS® Citations for all references: 2,481 TCR Web of Science® Average Citations per reference: 49 ACR SCOPUS® Average Citations per reference: 69 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 ... 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Stefan cel Mare University of Suceava, Romania
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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.
