# Fault Classification and Localization Scheme for Power Distribution Network
## 7 Fault Classification and Location with Renewable Energy Distributed Generation  
The electricity trajectory has evolved over the years with the more decentralized energy generation sources. In most cases, renewable energy sources (RES) have been used to enhance the electricity supply and security. However, there are technical dynamics that are introduced by these technologies. In the present work, we investigate the validity of the proposed fault classification and detection on a hybrid integrated system with RES. The wind energy (WE) and photovoltaic (PV) source parameters are given in Table 14. The integrated network used to validate our proposed scheme is depicted in Figure 10.  
The performance of SVM using the half, single, and two post-fault cycles is presented in Tables 15-17. The results presented in Tables 15-17 show that the precision accuracy of the SVM scheme using half a cycle's data post-fault is 97.7%, compared to 92.3% and 91.5% \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Type** & \multicolumn{2}{c}{**110 km**} & \multicolumn{4}{c}{**140 km**} \\ \hline  & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) \\ \hline LG & 0.00 & 0.00 & 0.00 & 0.10 & 5.00 & 0.00 \\ LL & 0.20 & 1.00 & 0.00 & 0.00 & 2.00 & 1.00 \\ LLG & 0.50 & 0.00 & 0.90 & 0.00 & 0.00 & 0.00 \\ LLL & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: AE at 110 and 140 km with a 20 \(\Omega\) fault impedance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **WE Source** & **PV Source** \\ \hline Voltage (kV) & 132 & 132 \\ Fault level (kA) & 1.34 & 0.91 \\ Power factor & 0.9 lag & 1.00 \\ Power (kW) & 15.50 & 6.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: RES parameters. Figure 10: Integrated power system with WE and PV sources. when using a single and two cycles of the post-fault data. In Table 18, the fault estimation results are presented. The fault estimation results are presented in Table 19. The scheme performed well with a minimum error, as depicted in Table 20, considering the dynamic change caused by the PV and WE technologies. In Table 21, we compare our scheme with some of the proposed schemes discussed in the literature. Based on the summary provided, our scheme has high accuracy and minimum estimation error compared to other schemes. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Fault Type** & **TP** & **FP** & **Precision** & **Recall** & **F-Measure** & **ROC** \\ \hline AG & 0.900 & 0.000 & 0.910 & 0.900 & 0.922 & 0.950 \\ BG & 0.910 & 0.000 & 0.912 & 0.965 & 0.955 & 0.944 \\ CG & 0.911 & 0.000 & 0.922 & 0.950 & 0.960 & 0.923 \\ AB & 0.920 & 0.000 & 0.904 & 0.900 & 0.925 & 0.962 \\ AC & 0.900 & 0.000 & 0.905 & 0.920 & 0.930 & 0.901 \\ BC & 0.902 & 0.000 & 0.909 & 0.925 & 0.922 & 0.921 \\ ABG & 0.919 & 0.000 & 0.920 & 0.977 & 0.920 & 0.922 \\ ACG & 0.910 & 0.030 & 0.930 & 0.922 & 0.922 & 0.908 \\ BCG & 0.908 & 0.080 & 0.955 & 0.940 & 0.925 & 0.922 \\ ABC & 0.957 & 0.010 & 0.966 & 0.930 & 0.940 & 0.933 \\ Avg & 0.914 & 0.012 & 0.923 & 0.933 & 0.923 & 0.929 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: SVM classification with a single cycle post-fault with PV and WE sources.