# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 4 Results and Discussion
### Uniform Solar Irradiance Condition (USI)  
Under uniform solar illumination, the level of radiation received by PV arrays is consistent, but it undergoes gradual and rapid variations over time. The irradiance profile utilized in this simulation to test the Proposed RSA algorithm under USI conditions is presented in Figure 4. The ambient temperature is maintained at a fixed value of \(T\) = 25 \({}^{\circ}\)C. As shown in the figure, irradiance changes successively and rapidly between different high and low levels, with both small and wide variations. For instance, there is a gradual decrease in irradiance from 1000 to 250 KW/m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameters** & **Iteration Number \(T\)** & **Population Size \(N\)** \\ \hline RSA & \(\beta\) = 0.005, \(\alpha\) = 0.1 & 4 & 5 \\ \hline DOA & \(\omega\), **a**, **b**, **c**, **d** and **e** are calculated in the program. & 9 & 6 \\ \hline GWO & \(\alpha\), \(\beta\), \(\gamma\) are calculated in the program. & 15 & 10 \\ \hline WOA & \(\mathbf{m}\) = 1.5, \(\mathbf{R_{c}}\) = rand, **l** is calculated in the program. & 5 & 7 \\ \hline PSO & \(\mathbf{c_{1}}\) = \(\mathbf{c_{2}}\) = 1.5, \(\omega\) = [0.4, 0.99] & 10 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setting of optimization algorithms’ parameters.  
Figure 4: Illumination profile of the uniform solar irradiance condition (USI).  
The comparison of simulation results is depicted in Figure 5, with certain portions of the resulting power curves being magnified in Figure 6 to enable a more thorough evaluation of the performance of the proposed RSA-MPPT algorithm. A comprehensive analysis of the algorithm's performance is presented in Table 2, including the power extracted at the MPP, settling time, power efficiency, and steady-state oscillation.  
At t = 0.55 s, a wide variation occurred when the irradiance gradually decreased from 1000 to 250 KW/m\({}^{2}\). The RSA algorithm achieved the highest efficiency of 99.47%, followed by PSO, DOA, GWO, and WOA, with efficiencies of: 98.79%, 97.68%, 95.92%, and 90.87%, respectively. The RSA-MPPT tracker settled at the MPP in the shortest time, taking only 25 ms, compared to DOA, WOA, GWO, and PSO, which took 40 ms, 60 ms, 50 ms, and 50 ms, respectively. The results demonstrate that, in both cases of rapid increase and decrease in PV irradiance, the power extracted by the RSA algorithm was the closest to the theoretical values, with good tracking rapidity and the highest efficiency of around 99.98%. The other tested algorithms were slower in finding the optimal MPP, and their efficiencies ranged from 90.87% to 99.83%.  
Figure 5: PV output power (P\({}_{\text{pv}}\)) under USI condition.  
Figure 6: Zoomed parts of the PV output power waveform.  
Additionally, as indicated in Figure 6, the developed RSA-MPPT algorithm exhibited the least fluctuation and steady-state power oscillation around the MPP point, with fluctuations below 20 watts. This indicates that in cases of variable irradiance, the new RSA method has the capability to reduce steady-state oscillation by 66.67% compared to DOA, 92.85% compared to WOA, 80% compared to GWO, and 75% compared to PSO.