# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results
### Results and Discussion  
The proposed methodology combines advanced computational techniques, experimental measurements, and error-handling strategies to model and optimize photovoltaic (PV) systems. The integration of a GA with a single-diode model forms the basis of the  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Temperature** (\({}^{\circ}\)**C ) & **GA** & **SLSQP** & **Cobyla** & **Rao-1** & **Rao-2** & **Rao-3** \\ \hline
**0** & **100** & **105** & **108** & **120** & **106** & **107** \\ \hline
**25** & **90** & **95** & **95** & **115** & **96** & **97** \\ \hline
**50** & **98** & **105** & **105** & **105** & **104** & **105** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Execution time comparison of optimization algorithms (in milliseconds).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **GA \%** & **SLSQP \%** & **Cobyla \%** & **Rao-1 \%** & **Rao-2 \%** & **Rao-3 \%** \\ \hline  & \multicolumn{6}{c}{**Temperature 0 \({}^{\circ}\)C**} \\ \hline Impp & 99 & 98 & 80 & 99 & 99 & 99 \\ \hline Vmpp & 98 & 98 & 92 & 98 & 98 & 98 \\ \hline Pmpp & 97 & 96 & 86 & 96 & 96 & 96 \\ \hline  & \multicolumn{6}{c}{**Temperature 25 \({}^{\circ}\)C**} \\ \hline Impp & 99 & 99 & 69 & 98 & 98 & 98 \\ \hline Vmpp & 99 & 99 & 84 & 99 & 99 & 99 \\ \hline Pmpp & 100 & 100 & 80 & 100 & 100 & 100 \\ \hline  & \multicolumn{6}{c}{**Temperature 50 \({}^{\circ}\)C**} \\ \hline Impp & 99 & 99 & 60 & 99 & 99 & 99 \\ \hline Vmpp & 96 & 96 & 75 & 96 & 96 & 96 \\ \hline Pmpp & 95 & 95 & 75 & 96 & 96 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison results of SLSQP, COBYLA, and Rao (1,2,3), based on the outputs.  
modeling approach. The ensuing discussion delineates the key aspects and implications of this methodology.  
The utilization of a GA involves a meticulous examination of its performance and sensitivity to various parameters. The GA's efficacy is contingent upon optimal hyperparameter tuning, particularly concerning population size, generation number, crossover probability, and mutation probability. The comprehensive error handling and validation procedure establishes a robust mechanism for comparing predicted and actual PV system outputs. The integration of a predefined error constant facilitates a quantitative assessment of the algorithm's performance. The discussion on the error-handling algorithm emphasizes its utility in identifying discrepancies and guiding further refinement of the genetic algorithm.  
The integrated procedure, harmonizing GA predictions with actual measurements, offers a holistic perspective on PV system modeling. The step-by-step approach, encompassing sensor data, GA predictions, and metered values, ensures a comprehensive evaluation. The methodology's practical implications lie in its ability to not only optimize PV system performance but also provide a systematic framework for continuous improvement.  
While the proposed methodology exhibits promising results, it is not without limitations. The simplified single-diode model may introduce inaccuracies, especially in extreme conditions. Future research could explore more intricate PV models to enhance accuracy. Additionally, the sensitivity of the GA to initial conditions warrants further investigation for robust real-world applicability.