## 8 Discussion  
The comparative analysis of the MPPT algorithms, summarized in Table 22, highlights distinct strengths and limitations across the different approaches.  
Conventional methods such as P&O, InC, and InC+P&O offer simplicity and fast convergence but consistently underperform in dynamic conditions, particularly under partial shading (PSC), where they tend to become trapped in local maxima. Their average efficiencies remained below 85% in PSC scenarios, confirming their limited adaptability.  
Fuzzy logic control (FLC) improved upon the traditional methods by providing better stability and slightly higher accuracy. While it showed moderate resilience under variable conditions, its tracking capability in PSC scenarios was still outperformed by more advanced techniques.  
AI-based methods, namely ANN and DQN, demonstrated superior overall performance across all the scenarios. These algorithms achieved high average efficiencies (above 91%), fast convergence, and excellent results, even under complex PSC conditions. Their ability to learn and generalize from environmental variations enabled them to reliably track the GMPP.  
Hybrid methods combining traditional techniques with genetic algorithms, such as InC + GA and P&O + GA, also yielded notable improvements in accuracy and adaptability. However, these gains came at the cost of increased convergence time due to the computational overhead of evolutionary optimization.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 0.1014 & 1.0773 & 0.0627 & 0.8993 & 98.5042 & 0.1958 \\ InC & 0.1017 & 1.0796 & 0.0628 & 0.8996 & 98.5004 & 0.1958 \\ Fuzzy & 0.0977 & 1.0385 & 0.0633 & 0.9023 & 98.5554 & 0.1988 \\ InC + P\&O & 0.0983 & 1.0369 & 0.0623 & 0.8938 & 98.5481 & 0.1933 \\ InC + GA & 0.0738 & 0.7924 & 0.0549 & 0.8245 & 98.8231 & 0.5854 \\ P\&O + GA & 0.1084 & 1.1356 & 0.0598 & 0.8733 & 98.3324 & 0.5760 \\ ANN & 0.2525 & 2.5886 & 0.0983 & 1.3707 & 91.4206 & 0.4003 \\ DQN & 0.2559 & 2.5900 & 0.1370 & 1.5501 & 96.2822 & 0.2341 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Performance metrics for Scenario PSC 3 (dynamic partial shading conditions).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Algorithm** & **MAE** & **IAE** & **MSE** & **ISE** & **Avg. Eff. (\%)** & **Conv. Time (s)** \\ \hline P\&O & 0.5829 & 6.1757 & 0.5657 & 6.4031 & 85.5496 & 0.3757 \\ InC & 0.5959 & 6.1727 & 0.5790 & 6.3991 & 85.1847 & 0.3751 \\ Fuzzy & 0.5532 & 5.6450 & 0.3808 & 4.1320 & 85.6871 & 0.5335 \\ InC + P\&O & 0.6064 & 6.1711 & 0.5899 & 6.3964 & 84.8987 & 0.3748 \\ InC + GA & 0.1988 & 2.0226 & 0.0479 & 0.5239 & 91.9530 & 0.4035 \\ P\&O + GA & 0.1992 & 2.0277 & 0.0515 & 0.5599 & 91.9349 & 0.4025 \\ ANN & 0.2053 & 2.0980 & 0.0538 & 0.6348 & 91.7499 & 0.1938 \\ DQN & 0.2043 & 2.0352 & 0.0596 & 0.5909 & 91.6215 & 0.9375 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Performance metrics for Scenario PSC 4 (extreme partial shading conditions).  
Overall, the results reinforce the effectiveness of AI-based and hybrid strategies for MPPT, particularly in challenging and rapidly changing operating environments.