Identifying Defects in the Transmission Lines by Neural Networks

Kherchouche Younes; Savah Houari

doi:doi:10.11648/j.ajee.20150302.12

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Identifying Defects in the Transmission Lines by Neural Networks

Kherchouche Younes, Savah Houari

Published in American Journal of Energy Engineering (Volume 3, Issue 2)

Received: 14 March 2015 Accepted: 24 March 2015 Published: 31 March 2015

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Abstract

The paper presents a new technique for the identification of defects in transmission lines using artificial neural networks. The technique uses the amplitude at the fundamental frequency voltage and current signals to one end of the line. A study is conducted to evaluate the performance of the fault identifier.

Published in	American Journal of Energy Engineering (Volume 3, Issue 2)
DOI	10.11648/j.ajee.20150302.12
Page(s)	16-20
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Defects, Identification, Transmission Line, Neural Networks

References

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[3]	Girgis A.A. and Brown, R.G.: Adaptive Kalman Filtering in Computer Relaying: “Fault Classification Using Voltage Models”, IEEE Trans. On Power Apparatus and Systems PAS-104 (1985) no. 5, pp. 1168-1177.
[4]	Dalstein T. And Kulicke B. “Neural network Approach to fault classification for high speed Protective relaying”, IEEE Trans. On Power Delivery, Vol.10 No.2, April 1995 pp. 1002-1011.
[5]	Kezunovic M., Rikalo I. And Sobajic D. J. “High speed fault detection and classification with neural nets”, Electric Power Systems Research, Vol. 34, 1995 pp. 109-116.
[6]	Poeltl A. And Fröhlich K.: “Two New Methods for Very Fast Type Detection by Means of Parameter Fitting and Artificial Neural Networks”, IEEE Transactions on Power Delivery PWRD vol.14 (1999) no. 4, pp. 1269-1275.
[7]	Pasand M.S and Zadeh H.K. “Transmission line fault detection & phase selection using ANN”, International Conference on Power Systems Transients, New Orleans, USA, IPST 2003, cdrom.
[8]	Aggarwal R. K., Xuan Q. Y., Dunn R.W., Johns A.T. and A. Bennett A.: “A Novel Fault Classification Technique for Double-circuit Lines Based on a Combined Unsupervised/Supervised Neural Network”, IEEE Transactions on Power Delivery PWRD vol.14 (1999) no. 4, pp. 1250-1255.
[9]	Lin W.M, Yang C.D., Lin J.H. and Tsa M.T. “A fault classification method by RBF neural network with OLS learning procedure”, IEEE Transactions on Power Delivery vol.16, no. 4, October 2001, pp. 473-477.
[10]	Logiciel matlab 7.7.0
[11]	Bouthiba Tahar “ Application des réseaux de neurones pour la détection des défauts dans les Lignes de transport ‘’, Conférence Internationale Francophone d’Automatique, ENSEIRB, Bordeaux (France), 30 mai - 1er juin 2006

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APA Style

Kherchouche Younes, Savah Houari. (2015). Identifying Defects in the Transmission Lines by Neural Networks. American Journal of Energy Engineering, 3(2), 16-20. https://doi.org/10.11648/j.ajee.20150302.12

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ACS Style

Kherchouche Younes; Savah Houari. Identifying Defects in the Transmission Lines by Neural Networks. Am. J. Energy Eng. 2015, 3(2), 16-20. doi: 10.11648/j.ajee.20150302.12

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AMA Style

Kherchouche Younes, Savah Houari. Identifying Defects in the Transmission Lines by Neural Networks. Am J Energy Eng. 2015;3(2):16-20. doi: 10.11648/j.ajee.20150302.12

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@article{10.11648/j.ajee.20150302.12,
  author = {Kherchouche Younes and Savah Houari},
  title = {Identifying Defects in the Transmission Lines by Neural Networks},
  journal = {American Journal of Energy Engineering},
  volume = {3},
  number = {2},
  pages = {16-20},
  doi = {10.11648/j.ajee.20150302.12},
  url = {https://doi.org/10.11648/j.ajee.20150302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20150302.12},
  abstract = {The paper presents a new technique for the identification of defects in transmission lines using artificial neural networks. The technique uses the amplitude at the fundamental frequency voltage and current signals to one end of the line. A study is conducted to evaluate the performance of the fault identifier.},
 year = {2015}
}

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TY  - JOUR
T1  - Identifying Defects in the Transmission Lines by Neural Networks
AU  - Kherchouche Younes
AU  - Savah Houari
Y1  - 2015/03/31
PY  - 2015
N1  - https://doi.org/10.11648/j.ajee.20150302.12
DO  - 10.11648/j.ajee.20150302.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 16
EP  - 20
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20150302.12
AB  - The paper presents a new technique for the identification of defects in transmission lines using artificial neural networks. The technique uses the amplitude at the fundamental frequency voltage and current signals to one end of the line. A study is conducted to evaluate the performance of the fault identifier.
VL  - 3
IS  - 2
ER  -

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Author Information

Kherchouche Younes

Faculty of Electrical Engineering, University Djillali Liabes , ICEPS (Intelligent Control Electrical Power System) Laboratory, Sidi Bel Abbes, Algeria
Savah Houari

Faculty of Electrical Engineering, University Djillali Liabes , ICEPS (Intelligent Control Electrical Power System) Laboratory, Sidi Bel Abbes, Algeria

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Plain Text BibTeX RIS

APA Style

Kherchouche Younes, Savah Houari. (2015). Identifying Defects in the Transmission Lines by Neural Networks. American Journal of Energy Engineering, 3(2), 16-20. https://doi.org/10.11648/j.ajee.20150302.12

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ACS Style

Kherchouche Younes; Savah Houari. Identifying Defects in the Transmission Lines by Neural Networks. Am. J. Energy Eng. 2015, 3(2), 16-20. doi: 10.11648/j.ajee.20150302.12

Copy | Download

AMA Style

Kherchouche Younes, Savah Houari. Identifying Defects in the Transmission Lines by Neural Networks. Am J Energy Eng. 2015;3(2):16-20. doi: 10.11648/j.ajee.20150302.12

Copy | Download

@article{10.11648/j.ajee.20150302.12,
  author = {Kherchouche Younes and Savah Houari},
  title = {Identifying Defects in the Transmission Lines by Neural Networks},
  journal = {American Journal of Energy Engineering},
  volume = {3},
  number = {2},
  pages = {16-20},
  doi = {10.11648/j.ajee.20150302.12},
  url = {https://doi.org/10.11648/j.ajee.20150302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajee.20150302.12},
  abstract = {The paper presents a new technique for the identification of defects in transmission lines using artificial neural networks. The technique uses the amplitude at the fundamental frequency voltage and current signals to one end of the line. A study is conducted to evaluate the performance of the fault identifier.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Identifying Defects in the Transmission Lines by Neural Networks
AU  - Kherchouche Younes
AU  - Savah Houari
Y1  - 2015/03/31
PY  - 2015
N1  - https://doi.org/10.11648/j.ajee.20150302.12
DO  - 10.11648/j.ajee.20150302.12
T2  - American Journal of Energy Engineering
JF  - American Journal of Energy Engineering
JO  - American Journal of Energy Engineering
SP  - 16
EP  - 20
PB  - Science Publishing Group
SN  - 2329-163X
UR  - https://doi.org/10.11648/j.ajee.20150302.12
AB  - The paper presents a new technique for the identification of defects in transmission lines using artificial neural networks. The technique uses the amplitude at the fundamental frequency voltage and current signals to one end of the line. A study is conducted to evaluate the performance of the fault identifier.
VL  - 3
IS  - 2
ER  -

Copy | Download