Neural networks identification of eleven types of faults in high voltage transmission lines

Laura Bautista F; Cesar Valencia N; Gerson Portilla F; Alfredo Sanabria; Carlos Pinto; Hernando González A; Carlos Arizmendi P; David Orjuela C

doi:10.1007/978-3-030-53021-1_18

Neural networks identification of eleven types of faults in high voltage transmission lines

Laura Bautista F, Cesar Valencia N, Gerson Portilla F, Alfredo Sanabria, Carlos Pinto, Hernando González A, Carlos Arizmendi P, David Orjuela C

Ingeniería Mecatrónica

Resultado de la investigación: Libro / Capitulo del libro / Informe › Libros de Investigación › revisión exhaustiva

Resumen

In power transmission systems faults returning leaving them offline. This problem generates an economic impact on the interested parties, partly because in certain cases transmission lines protections act in a delayed manner or because the data processing generated by electrical protections tends to be a tedious. Artificial intelligence personnel have implemented a number of methods aimed to provide solutions for detection, classification and localization of said faults. In this work, a multilayer neural network capable of performing the process of classifying 11 types of faults in power transmission lines was implemented. As a result, a graphical interface allows users to intuitively visualize the faults.

Idioma original	Inglés
Título de la publicación alojada	AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences
Subtítulo de la publicación alojada	Theory and Application
Editores	Dario Fernando Cortes Tobar, Vo Hoang Duy, Tran Trong Dao
Editorial	Springer
Páginas	175-184
Número de páginas	10
ISBN (versión impresa)	9783030530204
DOI	https://doi.org/10.1007/978-3-030-53021-1_18
Estado	Publicada - 2021
Evento	6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 - Bogota, Colombia Duración: 6 nov. 2019 → 8 nov. 2019

Serie de la publicación

Nombre	Lecture Notes in Electrical Engineering
Volumen	685 LNEE
ISSN (versión impresa)	1876-1100
ISSN (versión digital)	1876-1119

Conferencia

Conferencia	6th International Conference on Advanced Engineering Theory and Applications, AETA 2019
País/Territorio	Colombia
Ciudad	Bogota
Período	6/11/19 → 8/11/19

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10.1007/978-3-030-53021-1_18

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Bautista F, L., Valencia N, C., Portilla F, G., Sanabria, A., Pinto, C., González A, H., Arizmendi P, C., & Orjuela C, D. (2021). Neural networks identification of eleven types of faults in high voltage transmission lines. En D. F. Cortes Tobar, V. Hoang Duy, & T. Trong Dao (Eds.), AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application (pp. 175-184). (Lecture Notes in Electrical Engineering; Vol. 685 LNEE). Springer. https://doi.org/10.1007/978-3-030-53021-1_18

Bautista F, Laura ; Valencia N, Cesar ; Portilla F, Gerson et al. / Neural networks identification of eleven types of faults in high voltage transmission lines. AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. editor / Dario Fernando Cortes Tobar ; Vo Hoang Duy ; Tran Trong Dao. Springer, 2021. pp. 175-184 (Lecture Notes in Electrical Engineering).

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abstract = "In power transmission systems faults returning leaving them offline. This problem generates an economic impact on the interested parties, partly because in certain cases transmission lines protections act in a delayed manner or because the data processing generated by electrical protections tends to be a tedious. Artificial intelligence personnel have implemented a number of methods aimed to provide solutions for detection, classification and localization of said faults. In this work, a multilayer neural network capable of performing the process of classifying 11 types of faults in power transmission lines was implemented. As a result, a graphical interface allows users to intuitively visualize the faults.",

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Bautista F, L, Valencia N, C, Portilla F, G, Sanabria, A, Pinto, C, González A, H, Arizmendi P, C & Orjuela C, D 2021, Neural networks identification of eleven types of faults in high voltage transmission lines. En DF Cortes Tobar, V Hoang Duy & T Trong Dao (eds.), AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. Lecture Notes in Electrical Engineering, vol. 685 LNEE, Springer, pp. 175-184, 6th International Conference on Advanced Engineering Theory and Applications, AETA 2019, Bogota, Colombia, 6/11/19. https://doi.org/10.1007/978-3-030-53021-1_18

Neural networks identification of eleven types of faults in high voltage transmission lines. / Bautista F, Laura; Valencia N, Cesar; Portilla F, Gerson et al.

AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. ed. / Dario Fernando Cortes Tobar; Vo Hoang Duy; Tran Trong Dao. Springer, 2021. p. 175-184 (Lecture Notes in Electrical Engineering; Vol. 685 LNEE).

Resultado de la investigación: Libro / Capitulo del libro / Informe › Libros de Investigación › revisión exhaustiva

TY - GEN

T1 - Neural networks identification of eleven types of faults in high voltage transmission lines

AU - Bautista F, Laura

AU - Valencia N, Cesar

AU - Portilla F, Gerson

AU - Sanabria, Alfredo

AU - Pinto, Carlos

AU - González A, Hernando

AU - Arizmendi P, Carlos

AU - Orjuela C, David

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2021.

PY - 2021

Y1 - 2021

N2 - In power transmission systems faults returning leaving them offline. This problem generates an economic impact on the interested parties, partly because in certain cases transmission lines protections act in a delayed manner or because the data processing generated by electrical protections tends to be a tedious. Artificial intelligence personnel have implemented a number of methods aimed to provide solutions for detection, classification and localization of said faults. In this work, a multilayer neural network capable of performing the process of classifying 11 types of faults in power transmission lines was implemented. As a result, a graphical interface allows users to intuitively visualize the faults.

AB - In power transmission systems faults returning leaving them offline. This problem generates an economic impact on the interested parties, partly because in certain cases transmission lines protections act in a delayed manner or because the data processing generated by electrical protections tends to be a tedious. Artificial intelligence personnel have implemented a number of methods aimed to provide solutions for detection, classification and localization of said faults. In this work, a multilayer neural network capable of performing the process of classifying 11 types of faults in power transmission lines was implemented. As a result, a graphical interface allows users to intuitively visualize the faults.

KW - Confusion matrix

KW - GUI

KW - Multilayer perceptrons

KW - Neural networks

KW - Power transmission

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T3 - Lecture Notes in Electrical Engineering

SP - 175

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BT - AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences

A2 - Cortes Tobar, Dario Fernando

A2 - Hoang Duy, Vo

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PB - Springer

Y2 - 6 November 2019 through 8 November 2019

ER -

Bautista F L, Valencia N C, Portilla F G, Sanabria A, Pinto C, González A H et al. Neural networks identification of eleven types of faults in high voltage transmission lines. En Cortes Tobar DF, Hoang Duy V, Trong Dao T, editores, AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. Springer. 2021. p. 175-184. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-3-030-53021-1_18

Neural networks identification of eleven types of faults in high voltage transmission lines

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