Differential evolution with dynamic control factors for parameter estimation of photovoltaic models

Parameter estimation of photovoltaic (PV) models is an essential component in the design of a PV system with enhanced performance. Although many reliable solutions have been proposed by various authors in recent years, the estimation of PV model parameters is still an emerging area and remains an important focus. The estimation problem is formulated as a single objective function to be minimized based on the difference between the experimental and estimated current. Although different variants of differential evolution (DE) have been proposed in the past, in the current work, a DE with dynamic control factors (DEDCF) is proposed. DEDCF considers the experimental I–V datasets to estimate the parameters of single- and double-diode-based PV cell models and PV modules. The control factors include mutation and crossover factors, both of which need to undergo dynamic adjustment to arrive at better solutions. The results show the efficiency of DEDCF in comparison with other methods proposed in recent works for estimating the parameters of actual PV cells and modules.