Remote Monitoring of Outdoor High Voltage Insulator using Deep Learning-based Image Processing

Akzhol Baktiyar, Darkhan Baizhan, Mehdi Bagheri, Amin Zollanvari, Alimzhan Murzabulatov, Arailym Serikbay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The outdoor high voltage insulators are some of the essential parts for electrical and mechanical assistance of transmission lines. Monitoring the insulators' health frequently on a regular basis is an indispensable routine for ensuring uninterrupted power transmission. Traditionally, the monitoring is accomplished manually by linemen, which is time-consuming and implies additional logistics costs due to the long distances and various terrains associated with transmission lines. One of the most economic solutions can be condition monitoring by analysis of insulators' images using Unmanned Aerial Vehicles (UAVs). In this regard, this study focuses on outdoor high-voltage insulators' health monitoring by deep learning-based classification of hazardous surface conditions. To this end, Convolutional Neural Networks (CNNs) with hyperparameter optimization as well as fine-tuning pretrained deep learning models are employed to classify insulator surface conditions into one of the following four categories: clean, or covered either with snow, ice, or dust. Applying cross-validation external to the CNN hyperparameter optimization and the fine-tuning process of pretrained models yielded remarkable accuracies of 92.07% and 97.80%, respectively.

Original languageEnglish
Title of host publication21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings
EditorsZbigniew M. Leonowicz
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665436120
DOIs
Publication statusPublished - 2021
Event21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Bari, Italy
Duration: Sept 7 2021Sept 10 2021

Publication series

Name21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings

Conference

Conference21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021
Country/TerritoryItaly
CityBari
Period9/7/219/10/21

Keywords

  • convolutional neural networks
  • deep learning
  • High-voltage insulator
  • insulator surface condition
  • transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Environmental Engineering
  • Control and Optimization

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