## 6 Experimental Results
Once the optimum point is reached, all the points slow down and have a shorter jump. Thus, this characteristic will facilitate the proposed modified LFO to perform better in the searching effort under PSC. Figure 24 shows the experimental results of the proposed modified LFO under PSC 2. As can be seen, the proposed algorithm can achieve GMPP at 11 W. Here, the proposed  
Figure 23: Experimental results under PSC 1 of the proposed modified LFO, (**a**), (**b**), (**c**) reflect same result. Figure 17:0 PSCs pattern (**a**) P-V (**b**) I-V.  
modified LFO has 99% efficiency. Further, the proposed modified LFO is stable and has negligible steady-state oscillation. Figure 25 shows the experimental results for the proposed modified LFO under PSC 3. Based on the experimental result, the proposed modified LFO is also able to track GMPP at 16.1 W. The proposed modified LFO is stable and has no steady-state oscillation. Figure 17:1 Experimental results under PSC 2 of the proposed modified LFO, (**a**), (**b**) reflect same result. Figure 17:2 \(Cont.\)  
Next, the prototype is experimentally tested under dynamic conditions. The same patterns as those in the simulation are tested here, i.e., STC to PSC 1. All the numerical data of the patterns are listed in Table 4 and are loaded into the PV simulator. The dynamic patterns are shown in Figure 17:3  
Figure 27 shows the experimental results for the proposed modified LFO tested under dynamic conditions, i.e., STC to PSC 1. As can be observed, the proposed modified LFO is capable of detecting the LMPP under STC at irradiance 1000 W/m\({}^{2}\) with optimum power at 49.9 W, and then the operating condition is changed to PSC 1. Immediately after the change, the proposed modified LFO manages to detect the new GMPP at 17.4 W under PSC 1 with 3.0 s response time. The result in Figure 27 also indicates that the proposed modified LFO is stable, and negligible steady-state oscillation is observed. Table 5 summarizes the performance of the proposed modified LFO. It has excellent performance under uniform and non-uniform conditions. The proposed modified LFO has successfully tracked the LMPP under STC and GMPP under PSCs. The proposed modified LFO is also stable and has negligible steady-state oscillations. Figure 17:4 Dynamic set-up in the PV simulator at (a) 1000 W/m\({}^{2}\) (STC) changing over to (b) PSC 1. Figure 17:5 Experimental result under PSC 3 of the proposed modified LFO, (a), (b) reflect same result. The proposed algorithm is then compared with the two most famous methods which are P&O and PSO. P&O is chosen since this method is widely used either in practice or research, while PSO is chosen due to the fact that it is the most famous and simple search-based method. Table 6 shows a comparison between P&O, PSO, and the proposed algorithm. The proposed algorithm improves the efficiency of the system compared to P&O and PSO. The efficiency of all the tested conditions is higher than 98%. The operation of the proposed algorithm under partial shading is also guaranteed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MPPT** & \multirow{2}{*}{**Test Conditions**} & \multirow{2}{*}{**Irradiance (W/m\({}^{2}\))**} & \multicolumn{2}{c}{**Power (W)**} \\
**Algorithm** & & & **Simulation** & **Hardware** \\ \hline \multirow{6}{*}{Modified LFO} & \multirow{3}{*}{Uniform} & 1000 & 50.0 & 50.0 \\  & & 600 & 30.0 & 30.5 \\ \cline{3-5}  & & 200 & 10.0 & 10.1 \\ \cline{3-5}  & & 1000, 800, 400, 200 & 17.5 & 17.0 \\ \cline{3-5}  & Non-uniform & 400, 200, 1000, 200 & 11.1 & 11.1 \\ \cline{3-5}  & & 1000, 400, 200, 600 & 16.0 & 16.0 \\ \cline{3-5}  & Dynamic & 1000 to PSC 1 & 50.0\(\rightarrow\)17.5 & 49.9\(\rightarrow\)17.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Details of the proposed modified LFO algorithm. Figure 27: Dynamic changes from 1000 W/m\({}^{2}\) to PSC 1 for the proposed modified LFO, (a), (b), reflect same result.