# Fault Classification and Localization Scheme for Power Distribution Network
## 6 Results and Discussion  
In this section, we discuss the results of our proposed fault classification and location schemes. In the present work, WPD is used to analyze and decompose the fault current signal to improve the scheme's efficiency. The accuracy and performance of the WPD depend on the type of the mother wavelet. In [23], it was reported that the Daubechies (db.) mother wavelet performs better for transient signal analysis. In Table 3, different mother wavelets are tested to measure the classification and location schemes. It is observed in Table 4 that db4 shows high classification accuracy (99.5%) and a mean error of (0.10%), which is better compared to the other mother wavelets. The training and testing datasets used in the study are shown in Table 4. In Table 5, the SVM parameters obtained using the PSO are shown. In this  
The SVM performance results using different sample cycle delays are presented in Tables 6-8. Tables 6-8 show that the average precision classification accuracy is 99.5% when half a sample cycle is used compared to 95.4% and 96.5% when using one and two cycles post-fault. Thus, in our scheme, the half-cycle samples are used. Furthermore, the classification accuracy of the SVM in each phase is shown in Table 9. The classification accuracy is determined by calculating the ratio between the correctly classified instances and the total instances tested. Moreover, the number of samples is used to test the SVR scheme for the fault location. The results of the fault estimation distance using different fault impedance, fault angle incipient, and fault locations are depicted in Tables 10 and 11. The results in Tables 10 and 11 show that the SVR scheme is able to estimate the fault location with a minimum error irrespective of the fault angle and impedance. Moreover, Tables 12 and 13 depict the \begin{table}
\begin{tabular}{l l l l l l} \hline
**Fault Type** & **TP** & **FP** & **Precision** & **Recall** & **F-Measure** & **ROC** \\ \hline AG & 0.991 & 0.000 & 1.000 & 1.000 & 0.991 & 0.970 \\ BG & 0.920 & 0.000 & 0.991 & 0.990 & 0.990 & 0.966 \\ CG & 0.933 & 0.000 & 0.990 & 0.980 & 0.995 & 0.985 \\ AB & 0.985 & 0.000 & 0.998 & 0.985 & 0.977 & 0.990 \\ AC & 0.968 & 0.003 & 0.990 & 0.930 & 0.988 & 0.900 \\ BC & 0.987 & 0.000 & 1.000 & 0.975 & 0.970 & 0.988 \\ ABG & 0.980 & 0.014 & 0.990 & 0.957 & 0.966 & 0.955 \\ ACG & 0.981 & 0.010 & 1.000 & 0.911 & 0.960 & 0.975 \\ BCG & 0.971 & 0.035 & 0.999 & 0.980 & 0.991 & 0.961 \\ ABC & 0.981 & 0.043 & 0.990 & 0.990 & 0.955 & 0.992 \\ Avg & 0.969 & 0.011 & 0.995 & 0.970 & 0.978 & 0.968 \\ \hline \end{tabular}
\end{table} 
Table 6: SVM classification with half a cycle post-fault. \begin{table}
\begin{tabular}{l l l} \hline
**Data Matrix Sets** & **Fault Impedance (f)** & **Fault Angle (\(\blacklozenge\))** \\ \hline Training data & 1.5, 10, 15, 75, 100, 150, 220, 260 & 10\({}^{\circ}\) 15\({}^{\circ}\), 20\({}^{\circ}\), 40\({}^{\circ}\), 85\({}^{\circ}\) \\ Test data & 1.0 30, 45, 110, 165, 180, 210, 300 & 30\({}^{\circ}\), 80\({}^{\circ}\), 110\({}^{\circ}\), 140\({}^{\circ}\), 190\({}^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 4: Training and testing data. \begin{table}
\begin{tabular}{l l l l} \hline
**SVM** & **Fault Classification** & **Fault Location** \\ \hline Kernel function & Radial bias function & Radial bias function \\ Gamma (\(\gamma\)) & 0.42 & 0.42 \\ Cost (\(c\)) & 11.30 & 13.80 \\ Nu (\(mu\)) & 0.55 & 0.55 \\ \hline \end{tabular}
\end{table} 
Table 5: Feature selection of SVM using PSO. summary of the absolute error between the actual fault position and the estimated fault position. Most of the fault instances are accurately located with a minimum margin of error. The estimation results of the training data samples for a single phase to ground fault are depicted in Figure 8. From Figure 8, it can be seen that the SVR scheme has a good training outcome.