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

Research output: Events › Scientific events › peer-review

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.

Original language	English
Pages	175-184
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-030-53021-1_18
State	Published - 2021
Event	6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 - Bogota, Colombia Duration: 6 Nov 2019 → 8 Nov 2019

Conference

Conference	6th International Conference on Advanced Engineering Theory and Applications, AETA 2019
Country/Territory	Colombia
City	Bogota
Period	6/11/19 → 8/11/19

Keywords

Confusion matrix
GUI
Multilayer perceptrons
Neural networks
Power transmission

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

Cite this

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. 175-184. Paper presented at 6th International Conference on Advanced Engineering Theory and Applications, AETA 2019, Bogota, Colombia. https://doi.org/10.1007/978-3-030-53021-1_18

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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.",

keywords = "Confusion matrix, GUI, Multilayer perceptrons, Neural networks, Power transmission",

author = "{Bautista F}, Laura and {Valencia N}, Cesar and {Portilla F}, Gerson and Alfredo Sanabria and Carlos Pinto and {Gonz{\'a}lez A}, Hernando and {Arizmendi P}, Carlos and {Orjuela C}, David",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2021.; 6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 ; Conference date: 06-11-2019 Through 08-11-2019",

year = "2021",

doi = "10.1007/978-3-030-53021-1_18",

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pages = "175--184",

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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', Paper presented at 6th International Conference on Advanced Engineering Theory and Applications, AETA 2019, Bogota, Colombia, 6/11/19 - 8/11/19 pp. 175-184. https://doi.org/10.1007/978-3-030-53021-1_18

TY - CONF

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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U2 - 10.1007/978-3-030-53021-1_18

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

M3 - Eventos científicos

AN - SCOPUS:85089717470

SP - 175

EP - 184

T2 - 6th International Conference on Advanced Engineering Theory and Applications, AETA 2019

Y2 - 6 November 2019 through 8 November 2019

ER -

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

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Keywords

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