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

Producción científica: Capítulo en Libro/Reporte › Contribución a la conferencia

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 estadounidense
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
Volumen	685
ISBN (versión impresa)	9783030530204
DOI	https://doi.org/10.1007/978-3-030-53021-1_18
Estado	Publicada - ago. 11 2020
Evento	6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 - Bogota, Colombia Duración: nov. 6 2019 → nov. 8 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	11/6/19 → 11/8/19

Áreas temáticas de ASJC Scopus

Ingeniería industrial y de fabricación

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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. (2020). 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 (Vol. 685, 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. Vol. 685 Springer, 2020. pp. 175-184 (Lecture Notes in Electrical Engineering).

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title = "Neural Networks Identification of Eleven Types of Faults in High Voltage Transmission Lines",

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 2020, 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. vol. 685, 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, 11/6/19. 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. Vol. 685 Springer, 2020. p. 175-184 (Lecture Notes in Electrical Engineering; Vol. 685 LNEE).

Producción científica: Capítulo en Libro/Reporte › Contribución a la conferencia

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AU - Bautista F, Laura

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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.

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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. Vol. 685. Springer. 2020. 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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