# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 6 Conclusions  
This paper proposes a GIS partial discharge defect identification model based on the YOLOv5 algorithm. After obtaining the partial discharge PRPD map dataset through GIS partial discharge simulation experiments, the map is grayed out to facilitate target detection and classification of the map. Different training methods are used to optimize the model, and the recognition model proposed in this paper is compared with other deep learning detection models. The recognition model is applied to practical application to verify the effectiveness and accuracy of the model. Some conclusions can be drawn as follows:  
1. The different combinations of three training techniques, Mosaic data enhancement, cosine annealing decay, and smoothing labeling, are performed. The detection result and detection efficiency of the models under different training methods are compared and analyzed. The optimal detection model can be obtained by using a combination of cosine annealing attenuation and label smoothing training techniques.
2. The YOLOv5 model is compared with other deep learning target detection models. The results show that other models always have a lower accuracy rate in detecting a certain type of partial discharge defect, and the YOLOv5 model has higher target recognition accuracy and recognition result, with a mAP value of 95.89% and an FPS of 28.89.
3. Based on laboratory verification results, the GIS partial discharge defect detection model can identify multiple fault types at the same time. It can be applied to the scene  
Figure 14: GIS disintegration pictures.  
Figure 13: GIS partial discharge defect identification results. (**a**) channel 1, (**b**) channel 2, (**c**) channel 3, (**d**) channel 4.  
where one or more fault types of GIS failure occur at the same time, and it can also be applied to the scene where GIS failure occurs in a short period of time.
* The GIS partial discharge defect identification model proposed is trained and used for practical application, and the consistency of the identification results and the disintegration results verifies the accuracy of the model.  
Compared with traditional classification algorithms, the target detection algorithm can not only be used for real-time monitoring of GIS operation status, but also has high recognition accuracy for different types of PD defects. Multiple types of partial discharge defects can be detected, but due to the limited data of various types of partial discharge samples, the currently used multi-defective partial discharge detection samples are defective samples synthesized by image processing, which have certain limitations. The GIS partial discharge detection method based on YOLOv5 can effectively detect the results of multi-defective partial discharge, but there may also be a case of missed detection. Therefore, in future research, research on multi-defect partial discharge detection will be intensified, and more partial discharge maps will be collected under multiple defect types, and the sample size of multi-defect partial discharge defects will be increased, the model will be trained, and the detection effect of multi-defect partial discharge will be improved.  
Conceptualization, Y.L. and Z.Q.; methodology, Y.L. and Z.Q.; software, Z.W.; validation, Y.L. and Z.Q.; formal analysis, Z.Z.; investigation, Z.Z. and T.L.; resources, C.L.; data curation, Z.Z. and C.L.; writing--original draft preparation, Y.L. and Z.Q.; writing--review and editing, Y.L., Z.Q. and C.L.; supervision, C.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Science and Technology Project of State Grid Jiangsu Electric Power Company Limited, No. B310 EF215 FP7 Mobile GIS equipment partial discharge monitoring technology based on UHF signal intelligent analysis.  
Not applicable.  
All data used in this research can be provided upon request.