Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network
Journal of Electrical and Electronic Engineering
Volume 4, Issue 5, October 2016, Pages: 89-96
Received: Sep. 9, 2016; Accepted: Sep. 23, 2016; Published: Oct. 19, 2016
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Authors
Ei Phyo Thwe, Dept. of Electrical Power Engineering, Mandalay Technological University, Mandalay, Myanmar
Min Min Oo, Dept. of Electrical Power Engineering, Mandalay Technological University, Mandalay, Myanmar
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Abstract
The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.
Keywords
Artificial Neural Network, Fault Detection, Classification, Transmission Line
To cite this article
Ei Phyo Thwe, Min Min Oo, Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network, Journal of Electrical and Electronic Engineering. Vol. 4, No. 5, 2016, pp. 89-96. doi: 10.11648/j.jeee.20160405.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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