Solar PV technologies are broadly classified into three types monocrystalline, polycrystalline, and thin-film solar cells, which differ in material composition, conversion efficiency, cost, and domains of application. The PV module parameters have to be extracted very accurately to enable modeling, simulation, and performance prediction reliably. Single Diode Model (SDM) and Double Diode Model (DDM) represent two wide varieties of models that are adopted for representing the PV behavior. However, they are highly nonlinear equations, not easy to solve by conventional deterministic methods, which often suffer from the issue of convergence and entrapment in local minima. To overcome such drawbacks heuristic, Metaheuristics optimization methods like Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Human Evolutionary Optimization Algorithm (HEOA) etc. were used as a tool in parameter estimation. This paper introduces an innovative approach based on HEOA that captures fundamental concepts like learning and adaptation in humans and social cooperation. To test the efficacy of the HEOA it is applied on different modules of a solar cells with two different parameter sets like a five-parameter set SDM and a seven-parameter set DDM.It is carried out on three different solar panelsone monocrystalline solar panel named Mono PERC WSMD-545, a polycrystalline solar panel named Shell S75, and a thin-film solar panel named Shell ST40. To extract the parameters of the above specified cellssum of the squared error at critical points like open circuit voltage, short circuit current, and maximum power point are considered as objective function. From the results it has been observed that HEOA produce the better parameters with zero error as compared to the other algorithms like GWO and IGWO available in literature.
Ranga et al. (Wed,) studied this question.