Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters

Table 10

Advantages and disadvantages.

Method	Advantages	Disadvantages
Analytical	(i) Using the 5Pm method is very easy, the and are calculated with the empirical equations [25]; (ii) Very easy to implement on the computer for all software which allows calculation; (iii) The necessary time to extract the parameters is very short for the 5Pm method (duration is under ms); (iv) Does not require a powerful or dedicated PC, which leads to the small cost of the PC, and the used software can be a common one	(i) The accuracy of the parameters extracted decreases due to approximations used [8]; (ii) If some points of the - characteristic are used, the equation system obtained is nonlinear and it is necessary to use software that allows solving this (this can be costly); (iii) The complexity increases when the two diode model is used (seven equations are needed)
Metaheuristic	(i) The accuracy is higher than that of the analytical methods; (ii) The number of iterations can be reduced (for example, for the ISCE algorithm, the iteration number is 5000); (iii) The algorithms can be easily adapted for the two diode model, but the computational time will increase; (iv) Using algorithms as grey wolf, prey predator, and fire fly optimization, the performance of the parameters extraction can be improved [8]; (v) GA algorithm family can easy be part of the hybrid algorithm [65]; (vi) DE algorithm family has a high convergence; (vii) PSO can be improved if it is used together with Nelder-Mead methods, this hybridisation can reduce the computational resources [65]; (viii) SDA family has very good performance, and it can be easily used with other algorithms in hybrid structure; (ix) Improved version of SCE has performance comparable with HSDA	(i) The computational time can be high, it depends on the PC power and the metaheuristic algorithm (the number of the iterations and the complexity), there are algorithms with over 100000 iterations or even 350000-NM-MPSO algorithm [41]; (ii) The cost of the PC can be high and the dedicated software must be used; (iii) The performance of the most metaheuristic algorithms depend by the initial parameter range; (iv) Requires very good knowledge for implementation; (v) GA, SCE, TLBO, WOA, and BSA algorithms have a slow convergence; (vi) DE and PSO algorithms can converge prematurely; (vii) The computational time is high in case of the PSO algorithms; (viii) The computational time is relatively high, but using parallelization, it can be reduced; in the case presented, the time was reduced 6 times