# Research on State Diagnosis Methods of UHF Partial Discharge Sensors Based on Improved ViT
## 5 Conclusions  
State diagnosis of thermal high-frequency partial discharge sensors is a long-standing problem in industrial power grids. This paper has carried out relevant experimental research on this problem, and the conclusions are as follows: based on the ViT model, this paper has made improvements to the ViT model with regard to the poor resolution of picture data, as well as the calculation of a complex global attention mechanism and other problems. Local Window Attention, Shifted Windows, Hierarchical Design, cross-Window Connection and other methods of improvement, as well as the multi-head attention mechanism, are used. The extraction ability of the model's local features is enhanced, and the computational complexity of the model is effectively reduced. The feature extraction capability, based on the data collected from the feature library established by the UHF partial discharge sensors, has been enhanced. At the same time, this paper summarizes the four picture features (i.e., the four states) acquired by the UHF partial discharge sensor in the normal state and the four states in the fault state. In the final experimental results, it can be seen that the improved model shows better performance over the previous model on the validation set. The effectiveness of the improved model is verified.  
Evidently, the improved model still has some problems in real industrial scenarios. For example, in real industrial scenarios, under strong interference, multiple interference situations can arise in UHF partial discharge sensors. However, the current model always performs the classification task based on the existing fault types and does not classify situations involving multiple interferences. This is a problem that can be further studied in the future. Potential research directions for addressing the aforementioned issues could include interference characterization models, explainable AI, and meta-learning approaches. We could develop sub-models specifically designed to classify and understand interference types. Meanwhile, interpretable models could also be created to understand how interference impacts predictions. Then, meta-learning could be used to quickly adapt to new interference scenarios with minimal additional training. Meanwhile, the criteria of how to choose the optimal dividing number of images is another problem worth studying. A potential solution is to balance accuracy and training time effectively.  
Conceptualization, L.C.; methodology, Y.L.; software, Z.H and Y.L.; validation, C.C. and Y.W.; data curation, L.C. and C.C.; investigation, Y.W.; writing--original draft preparation,  
Figure 9: Accuracy of training.  
Z.H.; writing--review and editing. Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research and Development Project of the State Grid Corporation of China: Research and application of reliability improvement technology of substation sensors and sensor network considering the influence of multi-physics coupling (5700-202232441A-2-0-ZN).  
Not applicable.  
Restrictions apply to the datasets. The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
Author [PERSON] was employed by the company State Grid Shaanxi Electric Power Co., Ltd. Authors [PERSON] and [PERSON] were employed by the company State Grid Corporation of China, National Smart Grid Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.