## 5 Conclusions and Future Work  
In this study, an ML control method is developed specifically for grid-connected PV systems by training with data obtained by analyzing different grid states and conditions. In order to forecast the best control parameters using real time, including both fault and normal operation conditions of the grid-connected PVPP system, the ML approach is trained on historical data. Individual ML methods are modified to generate a hybrid protection technique and a DC chopper. A hybrid protection technique using the ML control approach has been successfully established, effectively reducing the impacts of three voltage dip types: 90%, 50%, and 25%. Among 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. The Medium Gaussian SVM classifier has the highest accuracy (98.37%) and F1-score (99.17%) for the DC chopper and CBFCL protection method among the 20 classifier methods. These values increase  
Figure 10: Dynamic response of the grid-connected PVPP system using the DCch-based Tree and DCch + CBFCL-based SVM protection approaches during of 25%: (**a**) active power, (**b**) DC-link voltage, (**c**) q-axis current, (**d**) d-axis current, (**e**) DC current, and (**f**) diode current. \begin{table}
\begin{tabular}{c c c c c} \hline
**Signal** & **Method** & **Peak Value** & **Lowest Value** & **Restoration Time (ms)** \\ \hline \multirow{3}{*}{P (kW)} & DCch-based Tree & 243.31 & 178.17 & 66.05 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 240.46 & 190.41 & 65.5 \\ \hline \multirow{3}{*}{DC-link voltage Vdc (V)} & DCch-based Three & 484.11 & 452.05 & 130.64 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 484.06 & 452.99 & 118.89 \\ \hline \multirow{3}{*}{DC current (p.u.)} & DCch-based Tree & 519.61 & 498.71 & 129.14 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 519.31 & 498.79 & 119.11 \\ \hline \multirow{3}{*}{Diode current (A)} & DCch-based Tree & 26.05 & 6.07 & 131.38 \\ \cline{2-5}  & DCch + CBFCL-based SVM & 25.99 & 6.37 & 120.27 \\ \hline \end{tabular}
\end{table} 
Table 8: Evaluation of two protection strategies’ tracking effectiveness for a 25% voltage dip in time t = 1 s.  
component compatibility by ensuring that the components used in the system perform best within a certain operating range; this helps the system operate more stably and reliably. When compared to the findings of another protection technique, the grid-connected PVPP system's DC-link voltage peak and lowest values utilizing the hybrid protection-based SVM approach are good values. Furthermore, when compared to the outcomes of other protection techniques, the hybrid protection-based SVM method provided the best settling time values for the grid-connected PVPP system in all of the tests. The effectiveness of the presented hybrid protection system shows the remarkable ability of ML approaches to precisely detect voltage dips and dynamically modify CBFCL to safeguard a grid-connected PVPP system under various voltage conditions. One of the main challenges of the proposed method is the long training time of the ML model. In addition, the ML model has a complex computational resource structure. Its implementation in real-world applications requires high computational power and advanced control systems, and this approach improves the stability of a grid-connected PVPP system. Therefore, all the scenarios needed for real-world studies may not have been available for this study. Future research should focus on overcoming these limitations by exploring more efficient ML algorithms that require less computational resources. It would be valuable to investigate alternative power electronics solutions and cost-reduction strategies for real-world applications. It would also be important to investigate the long-term stability and reliability of the performance of the grid-connected PVPP system under various grid conditions. This research received no external funding.