An Artificial Neural Network-Based Intelligent Fault Classification System for the 33-kV Nigeria Transmission Line

Mbamaluikem, Peter O and Awelewa, Ayokunle A. and Samuel, Isaac A. An Artificial Neural Network-Based Intelligent Fault Classification System for the 33-kV Nigeria Transmission Line. An Artificial Neural Network-Based Intelligent Fault Classification System, 13 (2). pp. 1274-1285. ISSN 0973-4562

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Abstract

Electric power Transmission lines are characterized by very lengthy transmission lines and thus are more exposed to the environment. Consequently, transmission lines are more prone to faults, which hinder the continuity of electric power supplied, increases the loss of electric power generated and loss of economy. Quick detection and classification of a fault hastens its Clearance and reduces system downtime thus, improving the security and efficiency of the network. Thus, this paper focuses on developing a single artificial neural network to detect and classify a fault on Nigeria 33-kV electric power transmission lines. This study employs feedforward artificial neural networks with backpropagation algorithm in developing the fault detector- classifier. The transmission lines were modeled using SimPowerSystems toolbox in Simulink and simulation is done in MATLAB environment. The instantaneous voltages and currents values are extracted and used to train the fault detector-classifier. Simulation results have been provided to demonstrate the efficiency of the developed intelligent systems for fault detection and classification on 33-kV Nigeria transmission lines. The performance of the detector-classifier is evaluated using the Mean Square Error (MSE) and the confusion matrix. The systems achieved an acceptable MSE of 0.00004279 and an accuracy of 95.7%, showing that the performance of the developed intelligent system is satisfactory. The result of the developed system in this work is better in comparison with other systems in the literature concerning Nigeria transmission lines

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 20:47
Last Modified:	02 Jul 2020 20:47
URI:	http://eprints.federalpolyilaro.edu.ng/id/eprint/883

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