International Journal of Innovative Science and Research Technology (IJISRT)· 2024
Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line
- 1,767citations
- 2024year
Short summary
An artificial neural network (ANN) model successfully identified five conditions (four fault types and no fault) on a 330kV Nigerian transmission line using voltage and current data.
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Key points
- An ANN model was developed to detect faults on a 330kV transmission line.
- The model uses voltage and current parameters for fault detection.
- It can differentiate between four fault types and a no-fault condition.
- A MATLAB Simulink model of the Gwagwalada-Katampe line in Nigeria was used to generate data.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. International Journal of Innovative Science and Research Technology (IJISRT), 2024 · DOI ↗
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Field: Control and Systems Engineering
Control and Systems EngineeringEngineering