Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line 2024

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.

Autores	Vilela, JMD; Faria, JR
Título	Industry 4.0 and 5g technology on firms network: a balanced competitive expansion conceptual model development
Afiliaciones	Universidade Federal Fluminense
Año	2022
DOI	10.14488/BJOPM.2022.1426
Tipo de acceso abierto	gold
Referencia	WOS:000811447300003
Artículo obtenido de:	WOS

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Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line

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