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Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things

Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things

Lishuo Zhang, Zhuxing Ma, Hao Gu, Zizhong Xin, Pengcheng Han
Copyright: © 2023 |Volume: 16 |Issue: 3 |Pages: 16
ISSN: 1935-570X|EISSN: 1935-5718|EISBN13: 9781668489529|DOI: 10.4018/IJITSA.324519
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MLA

Zhang, Lishuo, et al. "Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things." IJITSA vol.16, no.3 2023: pp.1-16. http://doi.org/10.4018/IJITSA.324519

APA

Zhang, L., Ma, Z., Gu, H., Xin, Z., & Han, P. (2023). Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things. International Journal of Information Technologies and Systems Approach (IJITSA), 16(3), 1-16. http://doi.org/10.4018/IJITSA.324519

Chicago

Zhang, Lishuo, et al. "Condition Monitoring and Analysis Method of Smart Substation Equipment Based on Deep Learning in Power Internet of Things," International Journal of Information Technologies and Systems Approach (IJITSA) 16, no.3: 1-16. http://doi.org/10.4018/IJITSA.324519

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Abstract

An accurate perception of the state of smart substation equipment is a strong guarantee for the reliable operation of the large power grid. This article proposes using deep learning for the device condition monitoring and analysis method in a power internet of things cloud edge collaboration mode. The speeded up robust features (SURF) feature detector is used at the edge of the network to accurately collect the interest points from the image data set, providing a reliable and complete sample data set support for the cloud-based deep learning network. Adding the attention mechanism module to the cloud improves the Yolov5 network model, enhance feature extraction, and increase the monitoring and analysis capabilities of the equipment. The simulation results show that the proposed method has achieved a recall rate of 91.21% and an accuracy rate of 90.54% for insulator fault evaluation indicators.