## 6 Conclusions  
This paper presents a novel method for predicting insulator leakage current on transmission lines using artificial intelligence and proposes an early warning strategy to establish a real-time salt contamination monitoring system, providing insights for pollution prevention for transmission lines. The study is based on data collected over thirty consecutive  
Figure 23: Results of the prediction of leakage current and pollution level on 3 March 2019 10:33–11:02 (Danger).  
Figure 22: Results of the prediction of leakage current and pollution level at 2019/03/03 10:20–10:49 (Warning).  
months from a severely polluted outdoor test station in Taiwan. The main findings are concluded below:  
1. The leakage current of insulators is closely linked to environmental parameters, serving as an indicator of insulator pollution levels.
2. Artificial intelligence algorithms can be applied to estimate leakage currents because they are monitored in real-time consecutively.
3. The study proposes three prediction models (SVR, GBR, and LSTM) to predict the leakage current of insulators. In this study, LSTM shows effectiveness in predictive tasks involving sequential data due to its ability to capture long-range dependencies and mitigate the vanishing gradient problem. Verification analysis and results reveal that LSTM attains the highest accuracy in learning operations, as evidenced by the case studies where evaluation metrics indicate lower _MSE_ and _MAE_ values, suggesting small errors in predictions.
4. The data used in the prediction model are from the measured data of the test station in actual operation. The results show that the proposed method is feasible.
5. In this study, despite the progress achieved through verification analysis, some enhancements are still necessary. From the results, the frequency of occurrence of leakage current peaks is relatively rare, resulting in minority peaks being seldom predicted. Thus, it might need to continuously collect data for long periods to improve the prediction model.
6. The proposed monitoring system offers real-time information for operational and maintenance personnel, facilitating cost-effective maintenance planning. It can prevent power outages due to salt contamination or pollution and reduce the workload for maintenance personnel. Moreover, upgrading the maintenance strategy from condition-based maintenance to time-based maintenance may greatly improve the efficiency of operation and maintenance for power lines.
7. The proposed novel method has the potential for application in critical transmission lines or towers with severe salt contamination, utilizing low-cost weather sensors to construct a real-time salt contamination monitoring system in the power grid.  
Conceptualization, Y.-T.L.; methodology, C.-C.K.; software, Y.-T.L.; validation, C.-C.K. and Y.-T.L.; formal analysis, Y.-T.L.; investigation, Y.-T.L.; resources, C.-C.K.; data curation, Y.-T.L.; writing--original draft preparation, Y.-T.L.; writing--review and editing, C.-C.K.; visualization, Y.-T.L.; supervision, C.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors would like to thank the editor and the anonymous reviewers for their helpful comments and suggestions and thank to the Taiwan Power Research Institute for providing invaluable support in conducting this research.  
The authors declare no conflicts of interest.