## 5 Case Studies  
Four cases are considered with four different shading patterns, as shown in Figure 7, and are proposed with different irradiance as mentioned in Table 2 to explore the PV characteristics under shading conditions. Figure 6: ChOA flow chart. Figure 7: Different irradiation patterns of PV system under shading conditions. The performance of the PV string is evaluated under partial shading conditions for three cases considering three different shading patterns as shown in Table 2, using various optimization-based MPPT techniques. Figure 8 represents the simulation circuit of four series-connected PV modules by varying the irradiation levels, temperature at 25 \({}^{\circ}\)C with the PV module and converter circuit details listed in Table 3.  
All characteristic parameters of the optimization algorithm are carefully selected to provide good convergence to the solved optimization task. The parameters used during simulations for CSO, GWO and ChoA algorithms are listed in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case No** & \begin{tabular}{c} **Arrangement of** \\ **PV Modules** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **PV Modules** \\ \end{tabular} & \
\begin{tabular}{c} **Irradiance Level** \\ \end{tabular} &
\begin{tabular}{c} **Temperature** \\ \end{tabular} \\ \hline \multirow{4}{*}{\(1\)} & Pattern-1 & 4 & \(1000\) W/m\({}^{2}\) & \multirow{4}{*}{25 \({}^{\circ}\)C} \\ \cline{3-3}  & & & \(800\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(600\) W/m\({}^{2}\) & \\ \hline \multirow{4}{*}{\(2\)} & Pattern-2 & 4 & \(1000\) W/m\({}^{2}\) & \multirow{4}{*}{25 \({}^{\circ}\)C} \\ \cline{3-3}  & & & \(500\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(1000\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(800\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(900\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(550\) W/m\({}^{2}\) & \\ \hline \multirow{4}{*}{\(4\)} & Pattern-4 & 4 & \(1000\) W/m\({}^{2}\) & \multirow{4}{*}{25 \({}^{\circ}\)C} \\ \cline{3-3}  & & & \(1000\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(1000\) W/m\({}^{2}\) & \\ \cline{3-3}  & & & \(1000\) W/m\({}^{2}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PV module with different partial shading patterns. Figure 8: Simulation circuit of four series-connected KC200 GT PV module. and Figure 12 (Simulation results using ChOA for pattern 1), the average convergence time of the GWO algorithm is relatively high. Even though the suggested chimp algorithm converges sufficiently quickly and catches only after a short period, the ability of ChOA, GWO and CSO algorithms to chase the GMPP under various shading situations is examined. It is observed from the simulation result that the output power obtained from the PV array using CSO is 521.41 W, using GWO is 527.44 W, and 531.198 W when ChoA is used. Figure 11 presents a performance comparison for the analysed metaheuristic algorithms. Figure 12: Simulation results using ChoA for the Pattern-1. Figure 10: Simulation result using CSO for the Pattern-1. The above Figure depicts the reduced oscillations and convergence time for the three algorithms, and it clearly shows that the proposed GWO have less oscillation and also less convergence time compared with the other algorithms, while the ChoA is slightly slower than the GWO algorithm. ChOA, GWO and CSO algorithms are simulated in MATLAB/SIMULINK to validate their expected performance under various partial shade patterns (\(G_{1}\) to \(G_{6}\)). Table 5 lists the outcomes of the statistical simulation. The variable power output with irradiation with different MPPT techniques under other shading patterns is presented in Figure 14. From Figure 14, \(G_{1}\) shading pattern will produce the solar photovoltaic system's highest power. Due to its speed and confidence, the simulation results of the various shading situations showed that ChoA performed better than other optimization strategies under partial shading settings.