# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Fault** & **Techniques** & **RMSE** & **MAPE** & \(\mathbf{R^{2}}\) & **NSEC** & **WIA** & **Training** & **Testing** \\ \multirow{2}{*}{AG} & MLP & 0.0656 & 0.0040 & 1.000 & 0.999 & 1.000 & 11.1563 & 0.0313 \\  & ELM & 0.3699 & 0.0253 & 0.998 & 0.997 & 0.999 & 0.0469 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{BG} & MLP & 0.0760 & 0.0086 & 1.000 & 0.999 & 1.000 & 11.7031 & \(\sim\)0.000 \\  & ELM & 0.0014 & 0.0001 & 1.000 & 1.000 & 1.000 & 0.0625 & 0.0625 \\ \hline \multirow{2}{*}{CG} & MLP & 0.0912 & 0.0080 & 0.999 & 0.999 & 1.000 & 11.7969 & 0.0313 \\  & ELM & 0.0014 & 0.0000 & 1.000 & 1.000 & 1.000 & 0.0625 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{AB} & MLP & 0.0685 & 0.0059 & 1.000 & 1.000 & 1.000 & 11.3281 & 0.0313 \\  & ELM & 0.0131 & 0.0010 & 1.000 & 1.000 & 1.000 & 0.0781 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{BC} & MLP & 0.1358 & 0.0111 & 0.999 & 0.999 & 0.999 & 10.0988 & 0.0313 \\  & ELM & 0.2347 & 0.0162 & 0.999 & 0.999 & 0.999 & 0.0625 & 0.0625 \\ \hline \multirow{2}{*}{CA} & MLP & 0.0550 & 0.0029 & 1.000 & 1.000 & 1.000 & 11.0313 & 0.0313 \\  & ELM & 0.0331 & 0.0018 & 1.000 & 1.000 & 1.000 & 0.0469 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{ABG} & MLP & 0.0483 & 0.0027 & 1.000 & 1.000 & 1.000 & 11.0938 & 0.0313 \\  & ELM & 0.0331 & 0.0329 & 0.996 & 0.991 & 0.997 & 0.0625 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{BCG} & MLP & 0.0896 & 0.0082 & 0.999 & 0.999 & 1.000 & 11.0781 & 0.0469 \\  & ELM & 0.0012 & 0.0002 & 1.000 & 1.000 & 1.000 & 0.0625 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{CAG} & MLP & 0.0522 & 0.0046 & 1.000 & 1.000 & 1.000 & 11.1406 & \(\sim\)0.000 \\  & ELM & 0.2265 & 0.0132 & 0.999 & 0.999 & 0.999 & 0.0313 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{ABC} & MLP & 0.0894 & 0.0058 & 0.999 & 0.999 & 1.000 & 10.9375 & \(\sim\)0.000 \\  & ELM & 1.1556 & 0.0504 & 0.988 & 0.977 & 0.994 & 0.0625 & \(\sim\)0.000 \\ \hline \multirow{2}{*}{ABCG} & MLP & 0.0900 & 0.0061 & 0.999 & 0.999 & 1.000 & 10.578 & 0.0313 \\  & ELM & 0.7536 & 0.0321 & 0.997 & 0.990 & 0.997 & 0.0625 & 0.0469 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance measurements of MLP and ELM for locating faults in a distribution system with an unbalanced load. the form of distributed generation units can also be studied. The method can be further improved by considering measurement noise and uncertainties in the transmission line and transformer parameters. Consideration of unsymmetrical transmission lines could be interesting to further study 3L and 3 LG faults. Another area of research is with regard to the statistical features. In this study, 144 features were considered for each fault, which could be reduced by identifying the most important statistical features from the less important features. Conceptualization, J.-W.S. ; Data curation, Y.D.M and M.S. ; Investigation, J.-W.S. ; Methodology, Y.D.M., Y.-D.L. and C.-C.K. ; Resources, J.-W.S. and M.S. ; Software, Y.D.M. and M.S. ; Supervision, C.-C.K. ; Validation, Y.-D.L. ; Writing--original draft, Y.D.M. All authors have read and agreed to the published version of the manuscript. No external funding. Support for this research from the Institute of Nuclear Energy Research (INER) Project and Ministry of Science and Technology of the Republic of China (Grant No. MOST 109-3116-F-042A-008-CC2) are gratefully acknowledged. The authors declare no conflict of interest.