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Published:
Sep 27, 2015DOI:
10.4314/njt.v34i4.26Keywords:
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Main Article Content
The Use of Artificial Neural Networks in the Theoretical Investigation of Faults in Transmission Lines
Abstract
This paper describes the development of a fast, efficient, artificial neural network (ANN) based fault diagnostic system (FDS) for the location of fault on transmission lines. The principal functions of this diagnostic system are: detection of fault occurrence, identification of faulted sections and classification of faults into types. This has been achieved through a cascaded, multilayer ANN structure using the back-propagation (BP) learning algorithm. This paper shows that the FDS accurately identifies High Impedance Faults, which are relatively difficult to identify with other methods. Test results are simulated and generated in MATLAB using Apo 132KV transmission line in Apo transmission substation, Abuja. These results amply demonstrate the capability of the FDS in terms of accuracy and speed with respect to detection, localization, and classification of faults in transmission lines.
