Abstract: To address the issue of inadequate diagnosis of power line faults, an automated power line fault diagnosis technology is put forward. In this context, the research leverages the object detection algorithm YOLOv5 to construct a fault diagnosis model and enhances its anchor box loss function. In addition, the study introduces digital twin models for fault point localization, and improves the recognition model by introducing GhostNet and attention mechanism, thereby enhancing the diagnostic performance of the technology in multi-objective scenarios. In the performance test of the loss function, the improved loss function performs the best in both regression loss and intersection over union ratio, with the average loss value and intersection over union ratio being 125 and 0.986, respectively. In multi-scenario fault diagnosis, the research model performs the best in accuracy and model loss, with values of 0.986 and 0.00125, respectively. In multi-scenario fault diagnosis, such as abnormal heating detection, when the number of targets is 4, the relative error of the research model is 0.86%, which is better than similar models. Finally, in the testing of frame rate recognition and diagnostic time, the research model shows excellent performance, surpassing similar technologies. The technology proposed by the research has good application effects. This study provides technical support for the construction of power informatization and line maintenance.

Siyu Wu and Xin Yan. “Power Line Fault Detection Combining Deep Learning and Digital Twin Model”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.5 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160569

@article{Wu2025,
title = {Power Line Fault Detection Combining Deep Learning and Digital Twin Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160569},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160569},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {Siyu Wu and Xin Yan}
}