A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines

Daniela Mora, Silvana Gamboa, Alberto Sanchez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This work proposes a methodology based on Deep Learning, such as recurrent neural networks (RNN) and long-short-term memory networks (LSTM) to classify faults in transmission lines in power systems. The implemented neural network is trained using data from the measurements of electric currents during the presence of faults that were obtained by simulation. The trained neural network is able to correctly classify faults using the current signature.

Idioma originalInglés
Título de la publicación alojadaProceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9798350380927
DOI
EstadoPublicada - 2023
Evento41st IEEE Central America and Panama Convention, CONCAPAN 2023 - Tegucigalpa, Honduras
Duración: 8 nov. 202310 nov. 2023

Serie de la publicación

NombreProceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023

Conferencia

Conferencia41st IEEE Central America and Panama Convention, CONCAPAN 2023
País/TerritorioHonduras
CiudadTegucigalpa
Período8/11/2310/11/23

Huella

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