Abstract
According to the manufacturer's I -V data, we need to obtain the best parameters for assessing the photovoltaic systems. Although much work has been done in this area, it is still challenging to extract model parameters accurately. An efficient solver called SFLBS is developed to deal with this problem, in which an inheritance mechanism based on crossover and mutation is introduced. Specifically, the memory pool for storing historical population information is designed. During the sub-population evolution, the historical population will cross and mutate with the contemporary population with a certain probability, ultimately inheriting information about the dimensions that perform well. This mechanism ensures the population's quality during the evolution process and effectively improves the local search ability of traditional SFLA. The proposed SFLBS is applied to extract unknown parameters from the single diode model, double diode model, three diode model, and photovoltaic module model. Based on the experimental results, we found that SFLBS has considerable accuracy in extracting the unknown parameters of the PV system problem, and its convergence speed is satisfactory. Moreover, SFLBS is used to evaluate three commercial PV modules under different irradiance and temperature conditions. The experimental results demonstrate that the performance of SFLBS is outstanding compared to some state-of-the-art competing algorithms. Moreover, SFLBS is still a reliable optimization tool despite the complex external environment. This research is supported by an online service for any question or needs to supplementary materials at https://aliasgharheidari.com. (C) 2021 The Authors. Published by Elsevier Ltd.