## 6 Comparative Performance Analysis  
In conventional MPP algorithm, there is a lot of fluctuation in the power curve of SPV due to continuous change in duty cycle with each step. However, after completion of every iteration, ICPSO algorithm provides a constant duty cycle which provides minimum fluctuations in the power curve of SPV, which is illustrated in Figure 11. In this figure, Proposed ICPSO is compared with the genetic algorithm based PSO (PSO-GA). The concept of implementation of PSO-GA is adopted from [28]. It is found that, if the fuzzy logic controller (FLC) concept is not applied to PSO-GA, MPP is achieved but with fluctuations. It has been perceived from the Figure 11 that, fast, steady-state and tracking of MPP with fewer fluctuations has been achieved by using ICPSO algorithm compared to PSO-GA algorithm. But there is a trade-off of a few watts power loss of maximum power has been observed during comparison of algorithms, which is illustrated one of the limitations of the proposed MPP algorithm. The significant outcomes are illustrated in Tables 1 and 2. Table 1 illustrated the comparative performance analysis under variable irradiation and constant temperature level. The tabled data established the effectiveness of the proposed hybrid MPP algorithm. It has been perceived that the performance of the proposed algorithm is not affected during sudden decrements in irradiance level and provide efficient output. Also, Table 2 illustrated the comparative performance analysis under variable temperature and constant irradiation level. The tabled data established the effectiveness of the proposed hybrid MPP algorithm. It has been perceived that the performance of the proposed algorithm is not affected during sudden increments in temperature level and provides efficient output. The PSO-GA based optimized FLC algorithm increases up to 8% of output power, whereas ICPSO increases up to 7% output power. Still, the computational complexity and time for tracking of MPP are less compared to the PSO-GA-FLC algorithm. Table 3 illustrated the comparative performance analysis of various algorithms for maximum power point tracking. It has been noted down that, proposed ICPSO tracked maximum power point at fast compared to other MPP algorithms. Figure 10: Duty cycle updation under different irradiance conditions. Regarding comparison, as mentioned above, it can be found that the proposed ICPSO MPP algorithm, when compared with the various MPP algorithms, results in the best trade-off between MPP tracking computational complexity, tracking speed, and ease of implementation. These all are achieved by the utilization of the concept of incremental conductance algorithm in PSO for the continuous updation of particle velocity and position for accurate tracking of MPP under nonuniform operating conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**MPPT Algorithms**} & \multicolumn{3}{c}{**SPV Output Power at Fix 25 \({}^{\circ}\)C**} \\ \cline{2-4}  & **1000 W/m\({}^{2}\)** & **600 W/m\({}^{2}\)** & **300 W/m\({}^{2}\)** \\ \hline P\&O Algorithm [28] & 90.13 kW & 53.68 kW & - \\ I\&C Algorithm [28] & 94.52 kW & 56.29 kW & - \\ GA-based optimized FLC [28] & 95.11 kW & 56.14 kW & - \\ PSO-based optimized FLC [28] & 96.15 kW & 56.78 kW & - \\ PSO-GA-based optimized FLC [28] & 98.85 kW & 58.64 kW & - \\ Proposed ICPSO & 97.3 W & 60 W & 94.2 W \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative performance analysis under variable irradiance and constant temperature level.