## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Value** \\ \hline Grid & Frequency & 60 Hz \\ \hline \multicolumn{3}{c}{Nominal phase-to-phase voltage Vn} \\  & (Vrms) & 25 kV \\ \hline PV array & Series-connected modules per string & 7 \\ \hline \multicolumn{3}{c}{Parallel strings} & 88 \\ \hline  & Voltage & 500 V \\ \hline \multicolumn{3}{c}{Power} & 250 kW \\ \hline CBFCL & R\({}_{\text{sh}}\) & 10 \(\Omega\) \\ \hline \multicolumn{3}{c}{C\({}_{\text{sh}}\)} & 50 uF \\ \hline \multicolumn{3}{c}{DC reactor (R\({}_{\text{dc}}\))} & 0.01\(\Omega\) \\ \hline \multicolumn{3}{c}{DC reactor (L\({}_{\text{dc}}\))} & 0.01 H \\ \hline \multicolumn{3}{c}{PV module} & Current at maximum power point (I\({}_{\text{max}}\)) & 5.69 A \\ \hline \multicolumn{3}{c}{Voltage at maximum power point (V\({}_{\text{max}}\))} & 72.9 V \\ \hline \multicolumn{3}{c}{Short-circuit current (I\({}_{\text{sc}}\))} & 6.09 A \\ \hline \multicolumn{3}{c}{Open circuit voltage (V\({}_{\text{oc}}\))} & 85.3 V \\ \hline \multicolumn{3}{c}{Cells per module (Ncell)} & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of grid-connected PVPP system. Accuracy, TPR, FPR, recall, precision, and F1-score values are given in Table 5. In this study, 3001 data points are randomly selected for the fault and normal conditions of the PVPP system. The obtained data are used to train 20 classifier methods separately. Among the 20 classifier algorithms, the Medium Gaussian SVM classifier has the best accuracy (98.37%) and F1-score (99.17%) for the DC chopper and CBFCL protection system. Therefore, the Medium Gaussian SVM classifier is selected for the DC chopper protection system and applied to the control of the protection system. Classification accuracy using the Medium Gaussian SVM for the grid-connected PVPP system is illustrated in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification Methods** & **Classifiers** & **Accuracy (\%)** & **TPR (\%)** & **FPR (\%)** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** \\ \hline \multirow{8}{*}{KNN} & Fine KNN & 97.6 & 98.76 & 61.29 & 98.76 & 96.93 & 98.76 \\ \cline{2-7}  & Medium KNN & 98 & 99.11 & 63.44 & 99.11 & 98.83 & 98.97 \\ \cline{2-7}  & Coarse KNN & 97.63 & 98.93 & 56.99 & 98.93 & 98.83 & 98.78 \\ \cline{2-7}  & Cosine KNN & 96.9 & 100 & 0 & 100 & 96.9 & 98.43 \\ \cline{2-7}  & Cubic KNN & 98 & 99.11 & 63.44 & 99.11 & 96.9 & 98.97 \\ \cline{2-7}  & Weighted KNN & 97.67 & 98.83 & 61.29 & 98.83 & 98.76 & 98.8 \\ \hline \multirow{8}{*}{Ensemble} & Boosted Trees & 97.6 & 98.73 & 62.37 & 98.73 & 98.76 & 98.76 \\ \cline{2-7}  & Bagged Trees & 97.57 & 98.8 & 59.14 & 98.8 & 98.69 & 98.75 \\ \cline{2-7}  & Subspace Discriminant & 96.9 & 100 & 0 & 100 & 98.69 & 98.43 \\ \cline{2-7}  & Subspace KNN & 97.6 & 98.76 & 61.29 & 98.76 & 98.76 & 98.76 \\ \cline{2-7}  & RUSBoostedTrees & 97.57 & 97.63 & 95.7 & 97.63 & 98.76 & 98.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 6: Grid-connected PVPP system classification accuracy with Coarse Tree: (**a**) ROC curve, (**b**) TPR and FNR values, and (**c**) number of observations. Figure 7: Grid-connected PVPP system classification accuracy with Medium Gaussian SVM: **(a)** ROC curve, **(b)** TPR and FNR values, and **(c)** number of observations.