Artificial Neural Networks for Intelligent Fault Location on the 33-kv Nigeria Transmission Line

Ayokunle, A. Awelewa and Peter, O. Mbamaluikem and Isaac, A. Samuel (2017) Artificial Neural Networks for Intelligent Fault Location on the 33-kv Nigeria Transmission Line. Artificial Neural Networks for Intelligent, 54 (3). pp. 147-155. ISSN 2231-5381

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Abstract

Transmission lines are generally susceptible to faults, and it is highly desirable and advantageous to locate and clear these faults at the fastest speed. Thus, the aim of this paper is to develop a fault locator using an artificial neural network that will detect, classify and locate a fault when it occurs on the 33-kV Nigeria transmission line network. The network is modeled and simulated in the MATLAB/Simulink environment. The training testing and evaluation of the intelligent locator is done based on a multilayer perceptron feedforward artificial neural network with backpropagation algorithm. The performance of the detectorclassifier and each locator was evaluated using Mean Square Error (MSE) and confusion matrix. The developed system is capable of detecting and classifying single line-to-ground faults, double lineto-ground faults, line-to-line faults, all the three lines short circuit faults and no fault condition; and locating line-to-ground, double line-to-ground faults and line-to-line faults. The detector-classifier achieved 91.5 % accuracy and 0.0022 MSE and the locators achieved 100% accuracy each and MSE of 0.000499, 0.000190157 and 0.000783 for single line-to-ground faults, double line-to-ground faults, and line-to-line faults, respectively. The result of the developed system in this work is better in comparison with other similar systems in the literature for locating faults on the Nigeria transmission line

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Mr Adedamola Bameke
Date Deposited:	02 Jul 2020 20:48
Last Modified:	02 Jul 2020 20:48
URI:	http://eprints.federalpolyilaro.edu.ng/id/eprint/885

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