# Fault Classification and Localization Scheme for Power Distribution Network
## 8 Conclusions  
In this paper, a fault classification and estimation technique in a distribution power system is proposed. The proposed scheme uses half a cycle from the post-fault signal to classify and locate the fault. The scheme employs the WPT signal processing technique to analyze and track signals and regions of interest. Statistical features are subsequently extracted from the decomposed signal using WPT. Afterward, a feature matrix is formulated and used as the input to train and test the SVM for fault classification and estimation purposes. The scheme was tested on a 132 kV distribution network. The results obtained show that the classification accuracy of the proposed scheme is 99.5% with a minimum error of estimation. Further, the scheme was able to correctly classify and locate different faults considering high impedances. Furthermore, we investigate the validity of our scheme on a grid-integrated system with the PV and WE technologies. The scheme produced good classification results with an accuracy of 97.7% and a minimum error of estimation. Future studies will consider fault detection with larger-scale penetration of renewable energy technologies. Further, future studies will entail scheme validation using practical data.  
Conceptualization, K.M.; Software, K.M.; Formal analysis, K.M. and N.W.N.; Writing--original draft, K.M.; Writing--review & editing, N.W.N.; Visualization, N.W.N.; Supervision,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Type** & \multicolumn{2}{c}{**45 km**} & \multicolumn{4}{c}{**90 km**} \\ \hline  & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) \\ \cline{2-6} LG & 0.01 & 1.40 & 00.0 & 0.00 & 0.10 & 1.20 \\ LL & 1.20 & 1.00 & 0.80 & 4.00 & 1.40 & 1.10 \\ LLG & 0.40 & 1.20 & 3.70 & 1.70 & 2.50 & 1.00 \\ LLL & 2.30 & 0.10 & 2.40 & 3.80 & 1.70 & 3.70 \\ \hline \hline \end{tabular}
\end{table}
Table 20: AE at 45 and 90 km with a 10 \(\Omega\) fault impedance with PV and WE sources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & \multicolumn{2}{c}{**Classification (\%)**} & \multicolumn{2}{c}{**Location (\%)**} \\ \hline Method [8] & 91.4 & \textless{}0.5 \\ Method [14] & - & \textless{}0.5 \\ Method [23] & 92.0 & - \\ Proposed method & 99.5 & \textless{}0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Scheme comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Type** & **No. of Instances** & **No. of Instances** & **No. of Instances** & **Accuracy (\%)** \\ \cline{2-5} LG & 25,400 & 25,400 & 0.000 & 100 \\ LL & 25,400 & 24,150 & 1250 & 95.1 \\ LLG & 25,400 & 24,326 & 1074 & 95.6 \\ LLL & 25,400 & 25,387 & 13.00 & 99.9 \\ Total & 101,600 & 99,263 & 2337 & 97.7 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Fault classification accuracy in different phases with PV and WE sources.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Type** & \multicolumn{2}{c}{**45 km**} & \multicolumn{2}{c}{**90 km**} \\ \hline  & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) & 0\({}^{\circ}\) & 30\({}^{\circ}\) & 60\({}^{\circ}\) \\ \cline{2-5} LG & 44.9 & 43.6 & 45.0 & 90.0 & 89.9 & 88.8 \\ LL & 43.8 & 44.0 & 44.2 & 86.0 & 88.6 & 88.9 \\ LLG & 44.6 & 43.8 & 41.3 & 88.3 & 87.5 & 89.0 \\ LLL & 42.7 & 44.9 & 42.6 & 86.2 & 88.3 & 86.3 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Fault estimation at 45 and 90 km with a 10 \(\Omega\) fault impedance with PV and WE sources.  
I.E.D.; Funding acquisition, I.E.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.