# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 5 Experimental Result
### Symmetrized Dot Pattern Result  
The SDP formula shows that the position of the midpoint of the polar coordinates is the focus of the algorithm. If the high frequency is the main component in the signal, the difference between the initial point and the next point in the time domain waveform is high, and the corresponding point in the polar coordinates represented by SDP will have a larger radius and smaller deflection angle and vice versa. Figures 20-23 show the SDP diagrams of the different fault types drawn using SDP. When the PD signal of the surge arrester was transformed from a time domain into a polar diagram, a more severe PD signal resulted in a pattern that was less like a snowflake. The PD signals of the different fault types were very different; therefore, this paper used CNN for training and recognition based on the forenamed characteristic. Since the original waveforms of valve aging and internal humidity were quite different from the normal shape, after the waveform was converted into an SDP graph, the graphs were also quite different, as shown in Figures 21 and 22. The original waveform of the salt damage of the ceramic barrier was slightly different from the normal shape. After getting converted, some of the graphics were observed to be similar to the normal shape, but the differences could also be observed, as shown in Figure 23.  
diagrams of surge arresters as recognition samples. There were 600 SDP diagrams used for training the CNN model, and each fault had 150 samples. To validate the effectiveness of the CNN model in identifying the fault types in the surge arresters, 240 SDP diagrams were used for the test, and each fault had 60 samples. According to the result shown in Table 3, the method could effectively classify different fault types of surge arresters and the recognition accuracy rate was 97.9% including normal (93.3%), aging (100%), internal humidity (100%), and salt damage (98.3%).  
The detection methods for image recognition in CNN and conventional machine learning were compared for their performance in identifying the surge arrester fault types, and the result is shown in Table 4. In terms of the training time, the conventional HOG + SVM and HOG + KNN methods only spent 5 s and 3 s on model training, respectively. The HOG + BPNN method required 121 s for training, making it the most time-consuming of all the detection methods. CNN spent 22 s on training. Although CNN was not the fastest among the four detection methods, its recognition accuracy was 97.9%, which was the highest among the four detection methods, followed by 95% for HOG + SVM and 94.6% for HOG + BPNN. The HOG + KNN method had the lowest accuracy at 91.2%.  
Figure 23: SDP diagram of salt damage to the ceramic insulator of a surge arrester (Type 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & **Test Pattern** & **Accurate Pattern** & **Accurate Rate (\%)** & **Recognizing Rate (\%)** \\ \hline Normal & 60 & 56 & 93.3 \\ \hline Aging of the internal valve & 60 & 60 & 100 \\ \hline Internal humidity & 60 & 60 & 100 \\ \hline Salt damage to the insulation & 60 & 59 & 98.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surge arrester fault recognition result.  
Figure 22: SDP diagram of a surge arrester with internal dampness (Type 3).