Electric Transmission System Fault Identification Using Modular Artificial Neural Networks for Single Transmission Lines

C. W. Asbery, Y. Liao

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

2 Scopus citations

Abstract

Identifying the fault location is usually the first step in system restoration. Accurate fault location is therefore essential in speeding up repair of faulted components and enhancing system reliability. Diverse fault location methods exist that have different strengths and weaknesses. This paper focuses on using artificial neural networks to classify and locate faults on single two-terminal transmission lines. There has not been sufficient existing work that elaborates how to choose the best neural network structures such as the number of hidden layers and the number of neurons in each layer. This paper aims to study the effects of various ANN structures (number of neurons in a single layer) and identify the most effective neural network structure. Study results based on simulated data are reported, which may provide guidance on designing efficient neural networks for classifying fault types and locating faults on single power transmission lines.

Original languageEnglish
Title of host publicationClemson University Power Systems Conference, PSC 2020
ISBN (Electronic)9781728193847
DOIs
StatePublished - Mar 2020
Event2020 Clemson University Power Systems Conference, PSC 2020 - Clemson, United States
Duration: Mar 10 2020Mar 13 2020

Publication series

NameClemson University Power Systems Conference, PSC 2020

Conference

Conference2020 Clemson University Power Systems Conference, PSC 2020
Country/TerritoryUnited States
CityClemson
Period3/10/203/13/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • artificial neural networks
  • fault identification
  • single transmission lines

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems and Management
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

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