Parameter Extraction of Photovoltaic Cells and Modules by INFO Algorithm

This study presents the extraction of unknown parameters of various photovoltaic (PV) cells and modules by using the weighted mean of vectors (INFO) algorithm. The parameter estimation of PV cells and modules is one of the most important issues in the design of effective PV power systems. Since the PV parameters are highly nonlinear and complex in nature, the estimation of these parameters also becomes a challenging optimization problem for designers. The main challenge is to obtain the most accurate estimation. In order to solve the problem in a unique way, the state-of-the-art metaheuristic algorithms that have not been tried so far in parameter extraction are chosen. The selected ones are the INFO optimization algorithm, the artificial hummingbird algorithm (AHA), the artificial ecosystem-based optimization (AEO) algorithm, the runge kutta (RUN) optimizer, and lastly the reptile search algorithm (RSA). The motivation here is to test as many algorithms as possible to reach to the most accurate solution. In addition, the gray wolf optimizer (GWO), the frequently used one in literature due to its superiority in parameter extraction applications, is selected to validate the results of the evaluated algorithms through comparison against the GWO. Moreover, the performances of these algorithms are compared with evaluation metrics consisting of minimum, mean, maximum, standard deviation, and statistical tests using Wilcoxon signed-rank test and Friedman test. At the end of the study, it is demonstrated that the INFO, statistically, produces the highest accuracy and reliable results. Due to its statistical success compared to other algorithms, the INFO is used to extract the parameters of a commercially available PV cells and modules. It is clearly shown that the parameters extracted by the INFO closely match the parameters provided by the manufacturer’s datasheet, which points out the superiority of the INFO algorithm in PV modeling.