Electric Transmission System Fault Identification Using Artificial Neural Networks

Christopher W. Asbery, Yuan Liao

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

4 Scopus citations

Abstract

Electric transmission systems are complex mesh networks that direct large amounts of energy from the point of generation to the point of consumption. Electric faults can cripple a system as power flows must be directed around the fault therefore leading to numerous potential issues such as overloading, customer service interruptions, or cascading failures. Therefore, identifying the classification and location of these faults as quickly and efficiently as possible is crucial. This work aims to utilize artificial neural networks to determine fault type and location based on measured voltages and currents. Eventually, once developed, this solution could be utilized for fault detection and classification on several transmission circuit topologies as well as with different fault types and resistances.

Original languageEnglish
Title of host publication2019 International Energy and Sustainability Conference, IESC 2019
ISBN (Electronic)9781728132914
DOIs
StatePublished - Oct 2019
Event2019 International Energy and Sustainability Conference, IESC 2019 - Farmingdale, United States
Duration: Oct 17 2019Oct 18 2019

Publication series

Name2019 International Energy and Sustainability Conference, IESC 2019

Conference

Conference2019 International Energy and Sustainability Conference, IESC 2019
Country/TerritoryUnited States
CityFarmingdale
Period10/17/1910/18/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • artificial neural networks
  • fault classification
  • fault location
  • transmission system

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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