Adolescent identity search algorithm for parameter extraction in photovoltaic solar cells and modules

Analysis and modeling of photovoltaic (PV) solar cells and modules based on experimentally measured data are critical for optimizing their design. The need for new algorithms to optimize the PV parameters, many of which owe their inspiration to the metaheuristic search concepts, is still a principal subject of interest and discussion. In this paper, an optimization algorithm that simulates the identity formation behavior of adolescents in the peer group, namely the adolescent identity search algorithm (AISA), was applied to identify the unknown parameters of PV models. In AISA, the updating process proceeds in the exploitation and exploration stages as follows. First, the new best position is generated by identifying and imitating the best identity features of a selected peer from the group to accelerate the exploitation process and produce better performance using a dynamic selection strategy. Second, any locally optimal solution is avoided in the exploration stage for the global optimal solution by adopting the negative/undesirable identity features observed in the peer group. In this context, AISA is applied to identify the unknown parameters of various benchmark test PV models, i.e., single-diode, double-diode, and PV module models. Obtained results showed that this algorithm performed very accurately since lower values of root mean square errors (RMSE) are achieved \((9.8602\times 10^{-4},2.4251\times 10^{-3},1.7298\times 10^{-3},1.6212\times 10^{-2},\ and\ 6.3666\times 10^{-4})\) when compared with other competitor algorithms. Further, a lower RMSE \((9.79352834\times 10^{-4})\) was obtained in the case of the double-diode model by adapting some parameters ranges. Also, the high closeness between the simulated current–voltage (I–V) curve is achieved by AISA compared with the experimental data.