Convolutional neural network for real-time main transformer detection. Issue 1 (1st March 2022)

• Record Type: Journal Article
• Authors: Zhou, Li; Shi, Tongqin; Huang, Songquan; Ke, Fangchao; Huang, Zhenxi; Zhang, Zhaoyang; Liang, Jinzheng
• Is Part Of: Journal of physics. Volume 2229:Issue 1(2022)
• Publication Date: 2022-03-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2229/1/012021
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: For substation constructions, the main transformer is the dominant electrical equipment, and its arrival and operation affect the progress of project directly. In the context of smart grid construction, in order to improve the efficiency of real-time main transformer detection, this paper proposes an identification and detection method based on the SSD algorithm. The SSD algorithm is able to extract the target device (such as main transformer) accurately and the Lenet algorithm module can analyse the features contained in the image. To improve the accuracy of the detection method, the image migration algorithm of VGG-Net is used to expand the negative samples of main transformers to improve the generalisation of the algorithm. Finally, the image set collected in the real substation projects is used for validation, and result shows that the method identifies main transformers more accurately, with high effectiveness and feasibility.