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

Daniela Mora; Silvana Gamboa; Alberto Sanchez

doi:10.1109/CONCAPANXLI59599.2023.10517550

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

Daniela Mora^*
, Silvana Gamboa^*
, Alberto Sanchez

^*Corresponding author for this work

Ingeniería Electrónica y Automatización

Escuela Politecnica Nacional

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350380927
DOIs	https://doi.org/10.1109/CONCAPANXLI59599.2023.10517550
State	Published - 2023
Event	41st IEEE Central America and Panama Convention, CONCAPAN 2023 - Tegucigalpa, Honduras Duration: 8 Nov 2023 → 10 Nov 2023

Publication series

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

Conference

Conference	41st IEEE Central America and Panama Convention, CONCAPAN 2023
Country/Territory	Honduras
City	Tegucigalpa
Period	8/11/23 → 10/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Deep learning
Electric Current
LSTM
RNN
Transmission Line

Access to Document

10.1109/CONCAPANXLI59599.2023.10517550

Cite this

Mora, D., Gamboa, S., & Sanchez, A. (2023). A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines. In Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023 (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CONCAPANXLI59599.2023.10517550

Mora, Daniela ; Gamboa, Silvana ; Sanchez, Alberto. / A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines. Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023).

@inproceedings{a51dc9c3b8d64ffd9c41a84d5d7e25ea,

title = "A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines",

abstract = "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.",

keywords = "Deep learning, Electric Current, LSTM, RNN, Transmission Line",

author = "Daniela Mora and Silvana Gamboa and Alberto Sanchez",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 41st IEEE Central America and Panama Convention, CONCAPAN 2023 ; Conference date: 08-11-2023 Through 10-11-2023",

year = "2023",

doi = "10.1109/CONCAPANXLI59599.2023.10517550",

language = "Ingl{\'e}s",

series = "Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023",

}

Mora, D, Gamboa, S & Sanchez, A 2023, A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines. in Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023, Institute of Electrical and Electronics Engineers Inc., 41st IEEE Central America and Panama Convention, CONCAPAN 2023, Tegucigalpa, Honduras, 8/11/23. https://doi.org/10.1109/CONCAPANXLI59599.2023.10517550

A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines. / Mora, Daniela; Gamboa, Silvana; Sanchez, Alberto.
Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines

AU - Mora, Daniela

AU - Gamboa, Silvana

AU - Sanchez, Alberto

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Deep learning

KW - Electric Current

KW - LSTM

KW - RNN

KW - Transmission Line

UR - https://www.scopus.com/pages/publications/85193252130

U2 - 10.1109/CONCAPANXLI59599.2023.10517550

DO - 10.1109/CONCAPANXLI59599.2023.10517550

M3 - Contribución a la conferencia

AN - SCOPUS:85193252130

T3 - Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023

BT - Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 41st IEEE Central America and Panama Convention, CONCAPAN 2023

Y2 - 8 November 2023 through 10 November 2023

ER -

Mora D, Gamboa S, Sanchez A. A Deep Learning Based Methodology Development for Fault Classification in Transmission Lines. In Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceeding of the 2023 IEEE 41st Central America and Panama Convention, CONCAPAN XLI 2023). doi: 10.1109/CONCAPANXLI59599.2023.10517550

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

Abstract

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UN SDGs

Keywords

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