Design and Research of Intelligent Operation Inspection and Monitoring System of Substation Based on Image Recognition Technology

Dongxu, Li; Li, Wang; Li, Jinxuan; Shaoyu, Xie; Wang, Long; Xie, Wenjie

doi:10.1109/acctcs53867.2022.00097

Cited by 2 publications

(2 citation statements)

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“…In the power system, a large number of power terminal equipment may experience wear or damage due to factors such as service life or environmental conditions. If these defects reach a high severity level, they can lead to equipment failure [1][2][3][4]. With the continuous increase in electricity consumption, the operational stability and level of intelligence of the power system face higher demands.…”

Section: Introductionmentioning

confidence: 99%

“…In light of the issues faced by current defect detection models, such as susceptibility to external environmental interference, inadequate recognition performance for devices of varying scales in real power scenarios, and incomplete feature extraction, this paper presents the following contributions: (1) Incorporating a variable convolution module into YOLOv5's backbone network to enhance the capture of finer details in the feature map. (2) Introducing sampling between different levels within YOLOv5's feature extraction component to facilitate information transmission and fusion across diverse scales. (3) Experimental validation and algorithm analysis are conducted to demonstrate the feasibility of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Visual Fault Detection Algorithm of Substation Equipment Based on Improved YOLOv5

Wu,

Xiao,

Liu

et al. 2023

Applied Sciences

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The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%