Solar cell parameter identification based on opposition-based chaotic whale optimization algorithm

Abstract

Whale optimization algorithm (WOA) has been successfully applied to address the engineering optimization problems. However, lacking of population diversity leads to that WOA is easy to fall into local optimum. To address this gap, this paper proposes opposition-based learning chaotic whale optimization algorithm (OBLCWOA), which is an improved form of WOA. OBLCWOA uses chaotic mapping to generate key random parameters and opposition-based strategy is utilized to enhance the diversity of the population. Those two strategies can effectively improve the convergence speed, convergence accuracy and global search capability. Subsequently, the OBLCWOA is simulated using 23 standard test functions, and the results show that the global search capability, convergence speed and convergence accuracy of the OBLCWOA are significantly improved compared with the WOA and QWOA. Finally, three solar cell models are constructed, the parameters of which is identified by OBLCWOA. The simulation data are compared with the measured data by constructing the fitness function. Experimental results provide evidence on the ability of OBLCWOA in solar cell parameter identification.