Authors :
Alhassan Musa Oruma; Ismaila Mahmud; Umar Alhaji Adamu; Simon Yakubu Wakawa; Gambo Idris; Mahmud Mustapha
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/y2y9extw
Scribd :
https://tinyurl.com/ympkyy9b
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR651
Abstract :
This research focused on identifying various
types of faults occurring on 330kV transmission lines
through the use of artificial neural networks (ANN). A
MATLAB model for the Gwagwalada-Katampe 330kV
transmission line in Nigeria was implemented to
generate fault datasets. Voltage and current fault
parameters were utilized to train and simulate the ANN
network architecture selected for each stage of fault
detection. Four types of faults were considered, along
with a fifth condition representing no fault. The results
illustrated the success of the developed model in
identifying various fault conditions and system
parameters on the Gwagwalada-Katampe 330kV
transmission line, modelled using MATLAB Simulink.
Keywords :
Fault Detection, Transmission Line, Artificial Neural Network.
This research focused on identifying various
types of faults occurring on 330kV transmission lines
through the use of artificial neural networks (ANN). A
MATLAB model for the Gwagwalada-Katampe 330kV
transmission line in Nigeria was implemented to
generate fault datasets. Voltage and current fault
parameters were utilized to train and simulate the ANN
network architecture selected for each stage of fault
detection. Four types of faults were considered, along
with a fifth condition representing no fault. The results
illustrated the success of the developed model in
identifying various fault conditions and system
parameters on the Gwagwalada-Katampe 330kV
transmission line, modelled using MATLAB Simulink.
Keywords :
Fault Detection, Transmission Line, Artificial Neural Network.
