A novel method for intelligent operation and maintenance of transformers using deep visual large model DETR + X and digital twin
2025
Intelligent Operation and Maintenance of Transformers Using Deep Learning
publication
10 minutes
Evidence: high
Author Information
Author(s): Zhang Xuedong, Sun Wenlei, Chen Ke, Song Shijie
Primary Institution: Xinjiang University
Hypothesis
Can deep visual models and digital twin technology improve the monitoring and maintenance of transformers?
Conclusion
The proposed DETR+X model significantly enhances the accuracy and efficiency of transformer monitoring and maintenance.
Supporting Evidence
- The DETR+X model achieved a classification accuracy of 100% for DGA feature maps.
- It outperformed existing models in small object detection tasks.
- The integration of digital twin technology allows for real-time monitoring.
- Data augmentation techniques improved model robustness.
- Deformable attention mechanisms enhanced feature extraction capabilities.
Takeaway
This study shows how using advanced computer models can help keep transformers running safely and efficiently by spotting problems early.
Methodology
The study integrates deep visual detection models with digital twin technology for real-time monitoring and maintenance of transformers.
Limitations
The model requires longer training times and may not effectively align multi-modal data.
Digital Object Identifier (DOI)
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