University of Khartoum

Neural Networks for Single Phase Faults Detection, Classification, and Location in Power Transmission Lines

Neural Networks for Single Phase Faults Detection, Classification, and Location in Power Transmission Lines

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Title: Neural Networks for Single Phase Faults Detection, Classification, and Location in Power Transmission Lines
Author: Ali, Mohammed Salih Hussien
Abstract: This thesis presents an artificial neural network approach for the detection, classification and isolation (location) of single phase faults occurring in electric power transmission lines. The objective is to implement a complete scheme for distance protection of a transmission line system. In order to perform this goal, the distance protection task is subdivided into different neural networks which are trained with back-propagation algorithm for fault detection, fault classification as well as fault location in different zones. Simulation is done using MATLAB Simulink to demonstrate that artificial neural network based method are efficient in detecting, classifying, and locating single phase faults on transmission lines and achieve satisfactory performance. A 50KM, 110 KV transmission line is used to create training and testing data for three neural networks mentioned above . The results show that the neural network is a powerful analytical tool for providing faults information that is more reliable and accurate than provided by the traditional relay .
Description: 55page
URI: http://khartoumspace.uofk.edu/handle/123456789/17938
Date: 2015-12-22


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