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

IEEE Access

Chunquan Li¹  Leyingyue Zhang¹  Rongling Chen¹  Zhiling Cui¹  Yufan Wu¹ 

[1] School of Information Engineering, Nanchang University, Nanchang, China;

关键词: Photovoltaic (PV);    moth flame optimization algorithm (MFO);    differential evolution algorithm (DE);    parameter identification;    soybean-rhizobium nodule symbiosis;   

DOI  :  10.1109/ACCESS.2020.3005711

来源: DOAJ

【 摘 要 】

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 well-established algorithms. Experimental results indicate that HSDE-MFO can acquire precise parameters of the three photovoltaic models and stable performance in 30 independent runs.

【 授权许可】

Unknown