Recent Development of Intelligent Shunt Fault Classifier for Nigeria 33-kV Power Lines
No Thumbnail Available
Date
Journal Title
Journal ISSN
Volume Title
Publisher
PB International
Abstract
Description
This paper presents a new approach to using artificial neural networks (ANNs) in improving the protection of
transmission lines. The proposed method uses instantaneous values of voltages and currents during normal and fault
conditions on a transmission line as inputs to four different neural network structures. The structures are then aptly
combined to yield a system that can detect and classify shunt faults with improved efficiency. The details of the design
procedure as well as various simulations carried out are provided in the paper. The performance of the developed
system is evaluated using two performance indices, viz., accuracy and mean square error (MSE), and the results show
that this approach is capable of detecting and classifying all possible shunt faults on the 33-kV Nigeria power lines in
less than 1ms with high level of accuracy. The performance of the system, when tested under various shunt fault types
with varying resistances and distances, shows that the system can be used to improve distance line protection in 33-
kV Nigeria power line.
Keywords
TK Electrical engineering. Electronics Nuclear engineering