Parameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithm

Abstract

Accurate modeling of photovoltaic (PV) modules is helpful in designing and assessing the energy production of PV systems. A new version of the differential evolution (DE) algorithm, called differential evolution with integrated mutation per iteration (DEIM), is proposed in this study to extract the seven parameters of a double-diode PV module model. This algorithm applies the attraction-repulsion concept of an electromagnetism-like algorithm to boost the mutation operation of the conventional DE algorithm. Moreover, a new adaptive strategy is proposed to tune mutation scaling and crossover rate for each generation. The proposed model is validated through experimental data and other models, which have been proposed in literature using various statistical errors. Results show that DEIM exhibits high accuracy and fast convergence speed compared with other methods. The average root mean square error, mean bias error, and absolute error at maximum power point of the proposed model are 1.713%, 0.149%, and 4.515%, respectively.