## 5 Simulation and Experimental Results of the Proposed System
### Simulation Results
#### 5.1.2 BLDC Motor Pump Performance  
Here, the starting and steady-state performances of the BLDC motor pump are exhibited. If there is any change in the level of irradiance, all the parameters in the BLDC motor pump vary according to the irradiance level. Usually, the BLDC runs above 1100 rpm. However, the required speed to pump water is below this speed at a low irradiance level of a minimum of 200 W/m\({}^{2}\). The proposed work is not intended to find the one among the best optimization algorithms, rather than to prove that the optimization technique can outperform the competitive fuzzy and elementary P&O algorithms. The Luo converter and its modified versions are considered to measure the comparative performance of the BLDC motor. Figure 8 shows the output current waveforms of the BLDC motor for phases A, B and C, respectively. In Figure 9, the back emf of the BLDC motor is shown. The back emf cuts the magnetic flux and opposes the current flowing through the conductor. The starting value of the back emf is zero, and the values depend on the speed of rotation of the armature conductor. Figure 8: Output current waveform of the brushless DC (BLDC) motor. Figure 9: The back emf of the BLDC motor. Figure 10 shows the starting and steady-state behaviour of the BLDC motor. The speed and electromagnetic torque reach their steady-state value when the maximum power is tracked by the GWO algorithm. Figure 11 shows the torque waveform of the BLDC motor before using the LUO converter. Here, the starting torque is high and is equal to 6 Nm, and the steady-state torque occurs at 0.12 s with a ripple of 5.5 Nm. Figure 12 shows the torque characteristics of the BLDC motor after using the LUO converter, as a result of which the ripples are reduced. Figure 11: Output load torque characteristics of the BLDC motor before using the LUO converter. Figure 12: Output load torque characteristics of the BLDC motor after using the LUO converter. The comparative results are given in Table 2, where it is seen that the proposed system is more efficient when compared to other MPPT algorithms and converter topologies, with an efficiency of 97.8%. The settling time and the output voltage gain are also better when compared to other methods. Usually, the optimization technique consumes significantly more time, which is, however, less than the manual tuning of the PI controller. Table 2 mentions the time taken by the system to settle after the tuning process is completed. The literature also shows that for a particular PI controller, it takes 10 s, whereas it is reduced to 4 s when using an optimization technique. Figure 13 shows the output voltage comparison of different MPPT methods. As the progression reaches the proposed method, the performance (higher output voltage) of the developed method (modified LUO converter with GWO) shows more improvement than the existing approaches. Figure 14 shows the efficiency comparison of the different MPPT methods. As the progression reaches the proposed method, the performance (higher efficiency) of the developed method (modified LUO converter with GWO) shows more improvement than the existing approaches. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**MPPT Methods/Converter.**} & \multicolumn{2}{c}{**Steady-State**} & \multicolumn{2}{c}{**Output Voltage**} & \multirow{2}{*}{**Efficiency (\%)**} \\ \cline{3-4} \multicolumn{2}{c}{} & **Settling Time (s)** & & **(V)** & \\ \hline \multirow{2}{*}{P\&O} & Elementary LUO Converter & 0.02 & 280 & 86 \\ \cline{2-4}  & Modified LUO Converter & 0.019 & 283 & 88 \\ \hline \multirow{2}{*}{Fuzzy} & Elementary LUO Converter & 0.018 & 285 & 92 \\ \cline{2-4}  & Modified LUO Converter & 0.016 & 289 & 94 \\ \hline \multirow{2}{*}{GWO} & Elementary LUO Converter & 0.01 & 293 & 95.6 \\ \cline{2-4}  & Modified LUO Converter & 0.009 & 298 & 97.8 \\ \hline \hline \end{tabular}
\end{table}