Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Extracting the optimum parameters of solar photovoltaic (PV) model using the experimental data of current-voltage is very critical in simulating, controlling, and optimizing the PV systems. One of the important problems encountered in modeling and simulating is to find a model that can extract parameters from PV models quickly, accurately, and reliably. Based on this motivation, the goal of this study is to suggest an improved algorithm, namely genetic algorithm based on non-uniform mutation (GAMNU), in order to approximate efficiently the parameters of solar cells and PV modules. In GAMNU, non-uniform mutation operator is used to maintain diversity in the explored solutions and the crossover operator follows an adaptive search strategy, which consists of searching the entire space at the beginning while maintaining a focused search when the population tends to converge in a certain region of the search space. The performance of the method is comprehensively evaluated on different solar cell models, including single and double diode, and single diode PV modules, of a R.T.C France silicon solar cell, ESP-160 PPW PV, STP6-120/36 and Photowatt-PWP201 module. The results obtained from single and double diode models for R.T.C France are respectively 9.8618x10(-4) and 9.8683x10(-4), and for Photowatt-PWP201, STP6-120/36 and ESP-160 PPW are 2.3824x10(-3) 2.382420230900 x 10(-3), 1.6735x10(-2) 1.6735786505085 x 10(-2) and 8.2942x10(-2) 8.2942 x 10(-2). The statistical obtained results show that the proposed method has very competitive performance in terms of accuracy and reliability when compared to other advanced algorithms. Therefore, the proposed algorithm is highly useful to extract the parameters of solar PV models.

Automated summary

This study proposes an improved algorithm, GAMNU, to efficiently approximate the parameters of solar cells and PV modules. The method is comprehensively evaluated on different solar cell models and shows competitive performance in terms of accuracy and reliability.