Application of machine learning methods in fault detection and classification of power transmission lines: a survey

Fatemeh Mohammadi Shakiba, S. Mohsen Azizi, Mengchu Zhou, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The rising development of power systems and smart grids calls for advanced fault diagnosis techniques to prevent undesired interruptions and expenses. One of the most important part of such systems is transmission lines. This paper presents a survey on recent machine learning-based techniques for fault detection, classification, and location estimation in transmission lines. In order to provide reliable and resilient electrical power energy, faster and more accurate fault identification tools are required. Costly consequences of probable faults motivate the need for immediate actions to detect them using intelligent methods, especially emerging machine learning approaches that are powerful in solving diagnosis problems. This paper presents a comprehensive review of various machine learning methodologies including naive Bayesian classifier, decision tree, random forest, k-nearest neighbor, and support vector machine as well as artificial neural networks such as feedforward neural network, convolutional neural network, and adaptive neuro-fuzzy inference system that have been used to detect, classify, and locate faults in transmission lines.

Original languageEnglish (US)
Pages (from-to)5799-5836
Number of pages38
JournalArtificial Intelligence Review
Volume56
Issue number7
DOIs
StatePublished - Jul 2023

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

Keywords

  • Adaptive neuro-fuzzy inference system
  • Artificial neural network
  • Convolutional neural network
  • Deep learning
  • Fault detection
  • Fault location estimation
  • Fault type classification
  • Machine learning
  • Transmission line

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