Fault identification method for substation power equipment based on infrared, visible light images, and YOLOv5 algorithm

Zhaoqun Liu; Yan Zhao; Donglai Wang

doi:10.1117/12.3006023

10 October 2023 Fault identification method for substation power equipment based on infrared, visible light images, and YOLOv5 algorithm

Zhaoqun Liu, Yan Zhao, Donglai Wang

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127990P (2023) https://doi.org/10.1117/12.3006023
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

For the issues of cable local heating, isolator ceramic disc breakage, lightning arrester and transformer local breakage among substation power equipment fault diagnosis, this study presented a YOLOv5 target detection approach based on deep convolutional neural network to identify and locate power equipment such as insulators, disconnect switch contacts, bushings, wire clips, lightning arresters and their abnormal heating areas. In the testing stage of the YOLOv5 process, the network divides the image in the database into 416*416 pixels. By using the Yolomark tool to annotate the image, the labels are fed into the deep learning convolutional neural network together with the sample set for training, and the ultimate module is generated after several iterations. Finally, the infrared and visible spectral materials of electronic facilities gathered by the operation and monitoring units with infrared thermographers are used for effect testing. Then the fault diagnosis and grading of various power equipment in substation are carried out. The detective findings are evaluated with the quicker regional convolutional neural network algorithm, YOLOv3 algorithm and SSD algorithm for comparison. Through the experimental analysis, the average accuracy of the model is 96.7%, which can achieve the fault diagnosis target of substation power equipment， and thus enhance the daily running, maintenance and management efficient of the power grid.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhaoqun Liu, Yan Zhao, and Donglai Wang "Fault identification method for substation power equipment based on infrared, visible light images, and YOLOv5 algorithm", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127990P (10 October 2023); https://doi.org/10.1117/12.3006023

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KEYWORDS

Education and training

Detection and tracking algorithms

Infrared radiation

Visible radiation

Infrared imaging

Dielectrics

Instrument modeling

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