Discharge severity evaluation of porcelain insulators based on UV images and YOLOv3
Discharge severity evaluation of porcelain insulators based on UV images and YOLOv3
- Author(s): L. Niu 1 ; S. Wang 1 ; F. Lü 1
- DOI: 10.1049/icp.2020.0048
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
Thank you
Your recommendation has been sent to your librarian.
- Author(s): L. Niu 1 ; S. Wang 1 ; F. Lü 1
-
-
View affiliations
-
Affiliations:
1:
North China Electric Power University School of Electrical and Electronic Engineering , Beijing , China
Source:
The 16th IET International Conference on AC and DC Power Transmission (ACDC 2020),
2021
p.
1879 – 1882
-
Affiliations:
1:
North China Electric Power University School of Electrical and Electronic Engineering , Beijing , China
- Conference: The 16th IET International Conference on AC and DC Power Transmission (ACDC 2020)
- DOI: 10.1049/icp.2020.0048
- ISBN: 978-1-83953-330-3
- Location: Online Conference
- Conference date: 02-03 July 2020
- Format: PDF
The safety operation of the power system is seriously threatened by the discharge and even flashover accidents of the porcelain insulator in wet and contamination conditions. Therefore, the research on the pollution discharge experiments of insulators were conducted in the artificial climate chamber to solve the problem above. Ultraviolet videos of different discharge states that based on the relative UV count and spot area parameters defined and their relationship between the leakage current were obtained. Then the ultraviolet image database was established and their discrimination of the four discharge severities were completed. Simultaneously, A deep learning platform was built based on YOLOv3 and then the classification of ultraviolet images was accomplished. The effects of learning rate and activation function on recognition accuracy and training errors were analysed. The network evaluation parameter and optimization methods are calculated and proposed. The recognition accuracy on the test set reaches 94.5%. This work can provide advice for engineering application.
Inspec keywords: neural nets; flashover; insulator contamination; learning (artificial intelligence); leakage currents; porcelain insulators
Subjects: Computer vision and image processing techniques; Image recognition; Optical, image and video signal processing; Power line supports, insulators and connectors