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 language | English |
|---|---|
| Title of host publication | Modeling and Optimization in Science and Technologies |
| Publisher | Springer Verlag |
| Pages | 81-88 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - Jan 1 2020 |
Publication series
| Name | Modeling and Optimization in Science and Technologies |
|---|---|
| Volume | 14 |
| ISSN (Print) | 2196-7326 |
| ISSN (Electronic) | 2196-7334 |
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ASJC Scopus subject areas
- Modelling and Simulation
- Medical Assisting and Transcription
- Applied Mathematics
Cite this
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 proceeding › Chapter
}
TY - CHAP
T1 - Deep-learning-based approach for outdoor electrical insulator inspection
AU - Pernebayeva, Damira
AU - James Pappachen, Alex
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-030-14524-8_6
DO - 10.1007/978-3-030-14524-8_6
M3 - Chapter
AN - SCOPUS:85064758082
T3 - Modeling and Optimization in Science and Technologies
SP - 81
EP - 88
BT - Modeling and Optimization in Science and Technologies
PB - Springer Verlag
ER -