Artificial neural network techniques apply for fault detecting and locating in overhead power transmission line

Patrick S. Pouabe Eboule, Jan Harm C. Pretorius, Nhlanhla Mbuli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

The study carried out here in this paper presents the application of artificial intelligence techniques to detect, classify and locate faults on power transmission line very high voltage. This paper compares the results of the techniques call Multi-Layers Perceptron (MLP) and Concurrent Neuro-Fuzzy used to detect, classify and locate the faults on two different very high voltage electrical lines of 735 kV line voltage, 600 km long for the first line and a second line of 400 kV, 120 km long. The advantages of these techniques used are that they permit to increase the level of reliability of a power line, to limit the lost time in the search for the defects when they occur and then to favorize a continuity of service so much sought by companies sign of economic prosperity.

Original languageEnglish
Title of host publicationAustralasian Universities Power Engineering Conference, AUPEC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538684740
DOIs
Publication statusPublished - Nov 2018
Event2018 Australasian Universities Power Engineering Conference, AUPEC 2018 - Auckland, New Zealand
Duration: 27 Nov 201830 Nov 2018

Publication series

NameAustralasian Universities Power Engineering Conference, AUPEC 2018

Conference

Conference2018 Australasian Universities Power Engineering Conference, AUPEC 2018
Country/TerritoryNew Zealand
CityAuckland
Period27/11/1830/11/18

Keywords

  • Artificial Neural Network
  • Concurrent Neuro Fuzzy
  • Defuzzification
  • Faults Classification
  • Faults Location
  • Faults detection
  • Fuzzification
  • Multi-Layer Perceptron
  • Power Transmission Lines

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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