Hybrid Symbiotic Differential Evolution Moth -Flame Optimization Algorithm for Estimating Parameters of Photovoltaic Models

Obtaining suitable parameters of photovoltaic models based on measured current-voltage data of the PV system is vital for assessing, controlling, and optimizing photovoltaic systems. To acquire specific parameters of photovoltaic models, we proposed a meta-heuristic algorithm named hybrid symbiotic differential evolution moth-flame optimization (HSDE-MFO) algorithm. The proposed algorithm implements our new proposed symbiotic algorithm structure (SAS). This structure is inspired by soybean-rhizobium nodule symbiosis in nature. The proposed SAS divides the population into two parallel working sub-groups, i.e., soybean group and rhizobium group. Soybean group that focuses on exploration is updated by the strategies in the DE algorithm; the rhizobium group that emphasizes on exploitation is renewed by the strategies in the MFO algorithm. Artificial particle selection strategy and artificial flames generation strategy are developed to generate high-quality mutant materials and high-quality flames, respectively. The above-proposed methods balance the exploration ability and exploitation ability and ensure a bionic structure of the proposed algorithm. Moreover, a new elite strategy is developed to offer a chaotic particle to further refine the quality of the current population. The proposed HSDE-MFO is employed to solve the parameters identification problem of photovoltaic models, i.e., single diode, double diode, and photovoltaic module and compared with recently wellestablished algorithms. Experimental results indicate that HSDE-MFO can acquire precise parameters of the three photovoltaic models and stable performance in 30 independent runs.