Intelligent recognition of transformer and converter components based on deep learning

Mingyong Xin; Changbao Xu; Yu Wang; Jianyang Zhu

doi:10.1117/12.2684564

19 October 2023 Intelligent recognition of transformer and converter components based on deep learning

Mingyong Xin, Changbao Xu, Yu Wang, Jianyang Zhu

Author Affiliations +

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270903 (2023) https://doi.org/10.1117/12.2684564
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

In view of the complex substation environment and the low efficiency and accompanying safety hazards of the traditional inspection method, technical innovation of the traditional inspection method can be carried out by the image recognition technology based on deep learning. This paper presents a deep learning-based recognition technique for transformer and converter image components. First, image calibration is performed on the transformer converter components, then simple features are combined into complex features using deep learning and feature extraction is completed using VGG 16. Finally, part recognition is performed by Faster-R-CNN framework. The proposed transformer equipment image recognition technique in the paper is tested and compared with the accuracy of previous recognition of different number of images. The results show that the proposed deep learning-based transformer/converter equipment image recognition technique can complete the recognition of transformer and converter components, and the recognition accuracy will improve with the increase of recognized images, which can realize the self-learning of image recognition.

Citation Download Citation

Mingyong Xin, Changbao Xu, Yu Wang, and Jianyang Zhu "Intelligent recognition of transformer and converter components based on deep learning", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270903 (19 October 2023); https://doi.org/10.1117/12.2684564

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