Research on the application of computer vision technology in automatic detection of substation equipment

Changyu Li; Gao Liu; Duanjiao Li; Ruchao Liao; Junsheng Lin

doi:10.1117/12.3054951

11 December 2024 Research on the application of computer vision technology in automatic detection of substation equipment

Changyu Li, Gao Liu, Duanjiao Li, Ruchao Liao, Junsheng Lin

Author Affiliations +

Proceedings Volume 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024); 134450I (2024) https://doi.org/10.1117/12.3054951
Event: International Conference on Electronics. Electrical and Information Engineering (ICEEIE 2024), 2024, Haikou, China

Abstract

As the power system rapidly develops, ensuring stable operation of substations enhances the reliability of the power grid. Traditional fault detection methods of substation equipment depend on manual inspection and regularly scheduled maintenance, which is inefficient and cannot be conducted in real time. In recent years, computer vision technology has rapidly advanced, providing new solutions for the automatic detection of substation equipment. It looks at, considering deep learning theory, the application of computer vision technology to the automatic detection of substation equipment; more precisely, through the use of CNNs, R-CNNs, and GANs, the performance relating to fault detection and classification. Through the deep learning model construction and feature extraction as well as fault classification design, test the effectiveness of the model. Thus, this research improves not only accuracy and efficiency in fault detection but also offers technical support for automation and intelligent management of substations. Research results: Deep learning technology has enormous application prospects in fault diagnosis of substation equipment. However, restricting issues in data collection, resources required for model training, and technical solution economic costs still need further study.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Changyu Li, Gao Liu, Duanjiao Li, Ruchao Liao, and Junsheng Lin "Research on the application of computer vision technology in automatic detection of substation equipment", Proc. SPIE 13445, International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 134450I (11 December 2024); https://doi.org/10.1117/12.3054951

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KEYWORDS

Deep learning

Visual process modeling

Computer vision technology

Image processing

Data modeling

Object detection

Convolutional neural networks

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