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: Chapter in Book/Report › Conference contribution

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 (US)
Title of host publication	AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences
Subtitle of host publication	Theory and Application
Editors	Dario Fernando Cortes Tobar, Vo Hoang Duy, Tran Trong Dao
Publisher	Springer
Pages	175-184
Number of pages	10
Volume	685
ISBN (Print)	9783030530204
DOIs	https://doi.org/10.1007/978-3-030-53021-1_18
State	Published - Aug 11 2020
Event	6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 - Bogota, Colombia Duration: Nov 6 2019 → Nov 8 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	685 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

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

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering

Access to Document

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. (2020). Neural Networks Identification of Eleven Types of Faults in High Voltage Transmission Lines. In 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).

@inproceedings{45e3e87481f44c06a9504decc760c36f,

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.",

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 = "2020",

month = aug,

day = "11",

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

language = "English (US)",

isbn = "9783030530204",

volume = "685",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "175--184",

editor = "{Cortes Tobar}, {Dario Fernando} and {Hoang Duy}, Vo and {Trong Dao}, Tran",

booktitle = "AETA 2019 - Recent Advances in Electrical Engineering and Related Sciences",

address = "Germany",

}

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. in 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).

Research output: Chapter in Book/Report › Conference contribution

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 - 2020/8/11

Y1 - 2020/8/11

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.

UR - http://www.scopus.com/inward/record.url?scp=85089717470&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85089717470&partnerID=8YFLogxK

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

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

M3 - Conference contribution

AN - SCOPUS:85089717470

SN - 9783030530204

VL - 685

T3 - Lecture Notes in Electrical Engineering

SP - 175

EP - 184

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

A2 - Cortes Tobar, Dario Fernando

A2 - Hoang Duy, Vo

A2 - Trong Dao, Tran

PB - Springer

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

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. In Cortes Tobar DF, Hoang Duy V, Trong Dao T, editors, 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

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this