Substation switching device identification method based on deep learning

Wang, Liang; Kou, Qilong; Zeng, Qinggai; Ji, Zhong-Hao; Zhou, Leiyue; Zhou, Shuai

doi:10.1109/docs55193.2022.9967743

Cited by 4 publications

(1 citation statement)

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“…Ciric et al [8] highlighted the advantages of thermography in monitoring the status of substation electrical cabinet door locks. Wang et al [9] proposed a deep learning-based substation switchgear status recognition method based on the YOLOv3 network. Therefore, the detection of electrical equipment lock status is of extreme importance to ensure the safety and reliability of substation equipment through preventive maintenance.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv7 Model Based on Visual Attention Fusion: Application to the Recognition of Bouncing Locks in Substation Power Cabinets

Wang

Zhang

et al. 2023

Applied Sciences

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With the continuous progress of intelligent power system technology, in order to meet the needs of substation operation and maintenance, a target detection algorithm is applied to identify the status of equipment switches. YOLOv7, as the latest achievement of YOLO (You Only Look Once) series algorithms, has good speed and accuracy in target detection tasks. However, when the generalized network is applied in a specific scenario, its advantages are not obvious due to its high weight and poor portability. In this paper, an improved GF-YOLOv7 network model is proposed to apply in the recognition of the status of bounce locks in a substation. The MobileViT module is used to improve the feature extraction ability of the backbone network. Referring to the CBAM feature attention mechanism, the channel attention module and the spatial attention module are used to design a more lightweight feature fusion network. The experimental results in the test set show that the proposed network can significantly reduce the network weight and improve the detection accuracy on the basis of a small reduction in the detection speed, and the accuracy reaches 97.8%, which can meet the needs of the detection task of substation bounce locks.

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Section: Introductionmentioning

confidence: 99%