## 5 Results
### Machine Learning-Based Fault Presence and Type Detection Models
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**FE Techniques** & **ML Algorithms** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Raw Data} & CNN & 0.00000 & 0.000000 & 0.000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.0000000 \\ \hline \multirow{4}{*}{AP} & CNN & 1.000000 & 1.000000 & 1.000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{FFT} & CNN & 1.000000 & 1.000000 & 1.000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{ACF} & CNN & 1.000000 & 0.950000 & 0.974359 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{PSD} & CNN & 1.000000 & 1.000000 & 1.000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\ \cline{1-1}  & DT & 1.000000 & 0.350000 & 0.518519 \\ \cline{1-1}  & RFC & 1.000000 & 0.450000 & 0.620690 \\ \hline \multirow{4}{*}{WT} & CNN & 0.000000 & 0.000000 & 0.000000 \\ \cline{1-1}  & SVM & 0.00000 & 0.000000 & 0.000000 \\ \cline{1-1}  & DT & 1.000000 & 0.250000 & 0.400000 \\ \cline{1-1}  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Different performance metric results of ML-based fault presence detection (classification layer 1) algorithm with various feature extraction methods with ML algorithms. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**FE Techniques** & **ML Algorithms** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Raw Data} & CNN & 0.000000 & 0.000000 & 0.0000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 1.000000 & 0.200000 & 0.333333 \\  & RFC & 1.000000 & 1.000000 & 1.000000 \\ \hline \multirow{4}{*}{AP} & CNN & 1.000000 & 1.000000 & 1.000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{FFT} & CNN & 1.000000 & 1.000000 & 1.000000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{ACF} & CNN & 1.000000 & 0.600000 & 0.750000 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 0.000000 & 0.000000 & 0.000000 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{PSD} & CNN & 1.000000 & 0.400000 & 0.571429 \\  & SVM & 1.000000 & 1.000000 & 1.000000 \\  & DT & 1.000000 & 0.400000 & 0.571429 \\  & RFC & 0.000000 & 0.000000 & 0.000000 \\ \hline \multirow{4}{*}{WT} & CNN & 0.000000 & 0.000000 & 0.000000 \\  & SVM & 0.062500 & 0.250000 & 0.100000 \\ \cline{1-1}  & DT & 0.062500 & 0.250000 & 0.100000 \\ \cline{1-1}  & RFC & 0.065789 & 0.250000 & 0.104167 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Different performance metric results of ML-based fault type detection (classification layer 2) algorithm with various feature extraction techniques with ML algorithms. This measurement considers 3 LG fault with respect to all power relays and meters. Figure 15: The prediction accuracy of ML-based fault type detection (classification layer 2) algorithm with different ML methods and feature extraction techniques for 3 LG fault with respect to SEL351S, SEL421 SV, SEL451, and SEL735. The processing time of the ML-based fault type identification is presented in Figure 16. The processing time of one datum is measured by considering the specific feature extraction method and ML algorithm. The proposed method is run on the 2023 MacBook Pro (Apple Inc., Cupertino, CA, USA), which has an Apple M2 Max chip, 12-core CPU, 30-core GPU, and 16-core Neural Engine. In Figure 16, the ACF method is excluded due to its extremely high processing time (around 3.5 s), which ruins the graph visually.