Fault Identification on Electrical Transmission Lines Using Artificial Neural Networks

Chris Asbery, Yuan Liao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Transmission lines are built to span over long distances, to which they are frequently exposed to many different situations that can cause abnormal conditions known as electrical faults. Electrical faults, when isolated, can cripple the transmission system as power flows are directed around these faults therefore leading to other numerous potential issues such as thermal and voltage violations, customer interruptions, or cascading events. Accurate fault classification and location is essential in reducing outage times and enhancing system reliability. Diverse methods exist and have different strengths and weaknesses. This paper aims to investigate the use of an intelligent technique based on artificial neural networks. The neural networks will attempt to determine the fault classification and precise fault location. Different fault cases are analyzed on multiple transmission line configurations using various phasor measurement arrangements from the two substations connecting the transmission line. The results can provide guidance on choosing the most efficient neural network structure and input measurements for transmission line fault classification and location.

Original languageEnglish
Pages (from-to)1118-1129
Number of pages12
JournalElectric Power Components and Systems
Issue number13-14
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.


  • artificial neural networks
  • fault location
  • fault type classification
  • feed forward neural networks
  • parallel transmission line
  • phasor measurement
  • single transmission line

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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