Deep-learning-based approach for outdoor electrical insulator inspection

Damira Pernebayeva, Alex James Pappachen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The outdoor electrical insulators are widely used in power transmission and distribution networks. They provide electrical isolation and mechanical support to conductors. Overhead insulators need to be inspected and monitored regularly to prevent faults and provide permanent electricity for consumers. The condition monitoring system for insulators is quite a challenging task due to the harsh operating conditions and the large number of insulators and their wide distribution in power transmission network. Traditional inspection methods for insulator evaluation are time-consuming, labor-intensive and costly. However, the inspection system based on deep learning model jointly with Unnamed Aerial Vehicle (UAV) can provide a remote, real-time condition evaluation in efficient and cost-effective manner. In this chapter, the frameworks of the deep neural network for insulator inspection are presented. The deep architecture including critical tasks such as insulator localization and insulator state evaluation is provided. The performance of existing deep learning models based on different architecture is also given.

Original languageEnglish
Title of host publicationModeling and Optimization in Science and Technologies
PublisherSpringer Verlag
Pages81-88
Number of pages8
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameModeling and Optimization in Science and Technologies
Volume14
ISSN (Print)2196-7326
ISSN (Electronic)2196-7334

Fingerprint

Insulator
Inspection
Electric power transmission networks
Learning
Power transmission
Electricity
Condition monitoring
Electric power distribution
Personnel
Antennas
Costs and Cost Analysis
Evaluation
Condition Monitoring
Deep learning
Costs
Distribution Network
Monitoring System
Conductor
Isolation
Fault

ASJC Scopus subject areas

  • Modelling and Simulation
  • Medical Assisting and Transcription
  • Applied Mathematics

Cite this

Pernebayeva, D., & James Pappachen, A. (2020). Deep-learning-based approach for outdoor electrical insulator inspection. In Modeling and Optimization in Science and Technologies (pp. 81-88). (Modeling and Optimization in Science and Technologies; Vol. 14). Springer Verlag. https://doi.org/10.1007/978-3-030-14524-8_6

Deep-learning-based approach for outdoor electrical insulator inspection. / Pernebayeva, Damira; James Pappachen, Alex.

Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. p. 81-88 (Modeling and Optimization in Science and Technologies; Vol. 14).

Research output: Chapter in Book/Report/Conference proceedingChapter

Pernebayeva, D & James Pappachen, A 2020, Deep-learning-based approach for outdoor electrical insulator inspection. in Modeling and Optimization in Science and Technologies. Modeling and Optimization in Science and Technologies, vol. 14, Springer Verlag, pp. 81-88. https://doi.org/10.1007/978-3-030-14524-8_6
Pernebayeva D, James Pappachen A. Deep-learning-based approach for outdoor electrical insulator inspection. In Modeling and Optimization in Science and Technologies. Springer Verlag. 2020. p. 81-88. (Modeling and Optimization in Science and Technologies). https://doi.org/10.1007/978-3-030-14524-8_6
Pernebayeva, Damira ; James Pappachen, Alex. / Deep-learning-based approach for outdoor electrical insulator inspection. Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. pp. 81-88 (Modeling and Optimization in Science and Technologies).
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