## 4 Results
### Testing Results of CNN with PD Data  
The PD data were recorded during the testing phase by gradually reducing the voltage from the RPDIV to the PDEV, with reductions performed in steps of one. The PDEV values for the test samples with void positions at the top, center, and bottom were 21, 26, and 21 kV, respectively.  
The PRPD patterns during this voltage reduction process exhibited distinct characteristics. Ten PRPD patterns were captured for a one-minute duration for each type of test sample from the lower RPDIV to the PDEV, which were labeled as Void_Top, Void_Center, and Void_Bottom. The PRPD patterns of these test samples demonstrated the influence of voltages from the PDEV to the RPDIV on the PD pattern shapes, as shown in Figure 9. The electrical stress within the insulation material increases as the voltage increases. This increased stress can cause PD activity to concentrate at certain discharge locations. PD may occur infrequently or at multiple sites at lower voltages, resulting in less regular patterns. These discharge sites, however, become more apparent and stable at higher voltages, resulting in more uniform and predictable PD patterns.  
The visibility and stability of the patterns clearly improved as the voltage increased from the PDEV, based on an analysis of the PRPD patterns. The reason is that the PDs have chaotic and random characteristics, and thus, the distribution of each PD pulse in the PRPD pattern will not be the same but distribute as a particular shape [5]. However, it is impractical to adjust the voltage levels during PD testing in real-world scenarios. A more feasible approach is to increase the measurement duration of the PRPD analysis.  
Figure 9: PRPD patterns for one-minute duration at different void positions.  
In addition, there can be instances of inrush surges in practical scenarios, in which the voltage levels momentarily exceed the RPDIV and then stabilize at normal or lower RPDIV values over time. This voltage fluctuation makes it challenging to precisely adjust the voltage according to the desired RPDIV. In such cases, increasing the measurement duration in the PRPD analysis becomes a more feasible and viable approach. This approach will be discussed in more detail in Section 4.3.  
After analyzing the PRPD pattern shapes, the defect-type testing of the CNN model that was trained using the RPDIV and PRPD patterns (as discussed in Section 4.1) was conducted with two datasets. The first dataset included all the lower RPDIV and PRPD patterns up to the PDEV, whereas the second dataset only focused on the PDEV and PRPD patterns of the three defect types. The results of the test scenarios are presented in Table 2.  
Considering all the lower RPDIV values, the overall testing accuracies for the different void positions were 79.3%, 57.1%, and 88.9% for Void_Top, Void_Bottom, and Void_Center, respectively. These accuracies indicated the ability of the trained CNN model to correctly classify the PRPD patterns associated with each void position.  
However, the results showed a decrease in accuracy for Void_Top (50%) and Void_Bottom (20%) when focusing specifically on the testing accuracy at the PDEV compared to the accuracies obtained when considering all the lower RPDIV values. Conversely, the testing accuracy for Void_Center remained at 88%, which may be due to the high PRPD data.  
The confusion matrix of the CNN when testing the overall data from the lower RPDIV to the PDEV is shown in Figure 10. The reason for the lower accuracy of the Void_Bottom defect type was due to the Void_Center accuracy being 42.9%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Defect Type** & **Overall Testing Accuracy (\%)** & **Testing Accuracy (\%) at PDEV Data** \\ \hline Void\_Top & 79.3 & 50 \\ \hline Void\_Center & 88.9 & 88 \\ \hline Void\_Bottom & 57.1 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Testing accuracies of CNN model with one-minute duration for PRPD data.  
Figure 10: Confusion matrix of CNN testing with lower RPDIV to the PDEV and PRPD patterns.