Research on transformer bolt detection based on machine vision

Yinmei Zhang; Kaihua Cheng; Gengyao Wu; Weitao Xu

doi:10.1117/12.3029753

21 June 2024 Research on transformer bolt detection based on machine vision

Yinmei Zhang, Kaihua Cheng, Gengyao Wu, Weitao Xu

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671U (2024) https://doi.org/10.1117/12.3029753
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

The calibration of transformers is a necessary aspect of maintaining the stability of the electrical power system. Nonstandard transformer calibrations are usually realized manually. However, manual calibrations are inefficient and inaccurate. To realize efficient and robust automated transformer calibration, we propose an edge feature-based template matching algorithm to detect the bolts of the voltage transformer. This algorithm combines the advantages of template matching and edge detection algorithms and is suitable for visual inspection in practical projects. This study provides a useful discussion for the reliable detection and identification of voltage transformer bolts. In the future, the algorithm parameters can be further optimized to adapt to a broader range of scenarios and application requirements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yinmei Zhang, Kaihua Cheng, Gengyao Wu, and Weitao Xu "Research on transformer bolt detection based on machine vision", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671U (21 June 2024); https://doi.org/10.1117/12.3029753

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