[1]TAO Yan,ZHANG Hui,HUANG Zhihong,et al.Accurate identification of thermal faults for typical components of distribution networks[J].CAAI Transactions on Intelligent Systems,2025,20(2):506-515.[doi:10.11992/tis.202311035]
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Accurate identification of thermal faults for typical components of distribution networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 506-515 Column: 人工智能院长论坛 Public date: 2025-03-05
Title:
Accurate identification of thermal faults for typical components of distribution networks
Author(s):
TAO Yan1;  ZHANG Hui2;  HUANG Zhihong3;  SHAN Chudong3;  XU Xianyong3
1. School of Electrical and Information Engineering, Changsha University of Technology, Changsha 410114, China;
2. School of Robotics, Hunan University, Changsha 410082, China;
3. State Grid Hu’nan Electric Power Company Limited Research Institute,
Keywords:
distribution network inspection; thermal fault diagnosis; multi-modal images; object detection; style transfer; IoU loss; image fusion; deep learning
CLC:
TP391
DOI:
10.11992/tis.202311035
Abstract:
A method for thermal fault diagnosis of distribution network components is presented, which includes the following: 1) A detection task conversion method used for target detection as a precursor to thermal fault detection. Training and prediction are performed using high-resolution visible light images, and the prediction information is converted into infrared images. 2) A style transfer method for scene adaptation in patrol tasks. 3) An improved IoU (intersection and union set) loss function, which reduces the effect of low-quality annotations on bounding box regression and improves model detection performance. Compared with previous methods, the proposed method uses multimodal image information and is not limited to low-resolution infrared images. The improved method achieves a detection accuracy of 88.1%, maintains real-time performance, and markedly reduces the cases of false and missed detections. The excellent target detection performance provides a solid foundation for thermal fault diagnosis, with an average temperature interpretation error not exceeding 0.8 °C.
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Last Update: 2025-03-05

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