Identifying Defects in the Transmission Lines by Neural Networks
American Journal of Energy Engineering
Volume 3, Issue 2, March 2015, Pages: 16-20
Received: Mar. 14, 2015; Accepted: Mar. 24, 2015; Published: Mar. 31, 2015
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Authors
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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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.
Keywords
Defects, Identification, Transmission Line, Neural Networks
To cite this article
Kherchouche Younes, Savah Houari, Identifying Defects in the Transmission Lines by Neural Networks, American Journal of Energy Engineering. Vol. 3, No. 2, 2015, pp. 16-20. doi: 10.11648/j.ajee.20150302.12
References
[1]
Héctor J.A.F, Ismael D.V. and Ernesto V.M. “Fourier and Walsh digital filtering algorithms forDistance protection”, IEEE Trans. On PAS, Vol. 11, No. 1, February, 1996, pp. 457-462.
[2]
Barros J. And Drake J.M. “Real time fault detection and classification in power systems using Microprocessors”, IEE Proc. Gener. Transm. Distrib., Vol. 141, No. 4, July 1994, pp. 315-322.
[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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