Segmentation and defect classification of the power line insulators: A deep learning-based approach

Arman Alahyari, Anton Hinneck, Rahim Tariverdizadeh, David Pozo

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

1 Citation (Scopus)

Abstract

Power transmission networks physically connect the power generators to the electric consumers. Such systems extend over hundreds of kilometers. There are many components in the transmission infrastructure that require a proper inspection to guarantee flawless performance and reliable delivery, which, if done manually, can be very costly and time consuming. One essential component is the insulator. Its failure can cause an interruption of the entire transmission line or a widespread power failure. Automated fault detection could significantly decrease inspection time and related costs. Recently, several works have been proposed based on convolutional neural networks, which address the issue mentioned above. However, existing studies focus on a specific type of insulator faults. Thus, in this study, we introduce a two-stage model that segments insulators from their background to then classify their states based on four different categories, namely: healthy, broken, burned/corroded and missing cap. The test results show that the proposed approach can realize the effective segmentation of insulators and achieve high accuracy in detecting several types of faults.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages476-481
Number of pages6
ISBN (Electronic)9781728185507
DOIs
Publication statusPublished - Nov 2020
Event2020 International Conference on Smart Grids and Energy Systems, SGES 2020 - Virtual, Perth, Australia
Duration: 23 Nov 202026 Nov 2020

Publication series

NameProceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020

Conference

Conference2020 International Conference on Smart Grids and Energy Systems, SGES 2020
Country/TerritoryAustralia
CityVirtual, Perth
Period23/11/2026/11/20

Keywords

  • Convolutional neural networks
  • Defect detection
  • Image classification
  • Insulators
  • Segmentation
  • Transmission lines

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