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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationAETA 2019 - Recent Advances in Electrical Engineering and Related Sciences
Subtitle of host publicationTheory and Application
EditorsDario Fernando Cortes Tobar, Vo Hoang Duy, Tran Trong Dao
PublisherSpringer
Pages175-184
Number of pages10
ISBN (Print)9783030530204
DOIs
StatePublished - 2021
Event6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 - Bogota, Colombia
Duration: 6 Nov 20198 Nov 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume685 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference6th International Conference on Advanced Engineering Theory and Applications, AETA 2019
Country/TerritoryColombia
CityBogota
Period6/11/198/11/19

Keywords

  • Confusion matrix
  • GUI
  • Multilayer perceptrons
  • Neural networks
  • Power transmission

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