## 4 Simulation Results  
Three different LVRT cases are analyzed in a 250 kW grid-connected PVPP model using Matlab/Simulink. The 250 kW grid-connected PVPP model consists of a PV panel, a T-type inverter, a transformer, and various values, as illustrated in Figure 4. The DC-link voltage and solar irradiation values are kept constant at 481 V and 1000 W/m\({}^{2}\), respectively. In this study, a DC chopper and CBFCL based on the ML algorithm are applied to regulate the performance of three different LVRT cases in a 250 kW grid-connected PVPP model. The ML control system for the two protection systems is designed for improved LVRT operation. The values of the parameters of the 250 kW grid-connected PVPP model are illustrated in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output Voltage** & \multicolumn{4}{c}{**Switch Statuses**} \\ \hline \(V_{an}\) & \(S_{a1}\) & \(S_{a2}\) & \(S_{a3}\) & \(S_{a4}\) \\ \hline \(V_{dc}/2\) & \(1\) & \(1\) & \(0\) & \(0\) \\ \hline \(0\) & \(0\) & \(1\) & \(1\) & \(0\) \\ \hline \(-V_{dc}/2\) & \(0\) & \(0\) & \(1\) & \(1\) \\ \hline \end{tabular}
\end{table} 
Table 2: Output voltage levels and switch statuses of the T-type inverter for single phase. Figure 5: The simulation circuit of the three-phase T-type inverter. MATLAB Classification Learner Toolbox has four classification methods, which are named Tree, SVM, KNN, and Ensemble. These classification methods also have many classifiers. 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. As a result of the training, accuracy, TPR, FPR, recall, precision, and F1-score values are obtained, and these are given in Table 4. Among the 20 classifier algorithms, the Coarse Tree classifier has the best accuracy (98.31%), TPR (98.9%), FPR (78.5%), precision (99.31%), and F1-score (99.12%) for the DC chopper protection system. Therefore, the Coarse Tree classifier is selected for the DC chopper protection system and applied to the control of the protection system. Classification accuracy using a Coarse Tree for the grid-connected PVPP system is illustrated in Figure 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classification Methods**} & \multirow{2}{*}{**Classifiers**} & **Accuracy** & **TPR** & **FPR** & **Recall** & **Precision** & **F1-Score** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{4}{*}{Tree} & Fine Tree & 97.73 & 98.76 & 65.59 & 98.8 & 98.9 & 98.83 \\ \cline{2-7}  & Medium Tree & 97.7 & 98.66 & 67.74 & 98.7 & 98.9 & 98.81 \\ \cline{2-7}  & Coarse Tree & 98.31 & 98.9 & 78.5 & 98.93 & 99.31 & 99.12 \\ \hline \multirow{4}{*}{SVM} & Linear SVM & 97.63 & 98.9 & 58.06 & 98.9 & 99.31 & 98.78 \\ \cline{2-7}  & Quadratic SVM & 78.37 & 78.85 & 63.44 & 78.85 & 98.54 & 87.6 \\ \cline{2-7}  & Cubic SVM & 20.96 & 19.84 & 55.91 & 19.84 & 98.54 & 32.73 \\ \cline{2-7}  & Fine Gaussian SVM & 97.97 & 98.38 & 84.95 & 98.38 & 99.51 & 98.95 \\ \cline{2-7}  & Medium Gaussian SVM & 97.67 & 98.97 & 56.99 & 98.97 & 99.51 & 98.8 \\ \cline{2-7}  & Coarse Gaussian SVM & 96.93 & 100 & 1.08 & 100 & 96.93 & 98.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy for DC chopper protection system using various classifiers.