Parameters identification of solar cell models using generalized oppositional teaching learning based optimization

• A new optimization method called GOTLBO is proposed.
• A GOTLBO-based parameter identification method for solar cell models is also developed.
• GOTLBO is comprehensively evaluated through benchmark functions and parameter identification problems.
• Simulation results demonstrate the superiority of GOTLBO.

This paper presents a new optimization method called GOTLBO (generalized oppositional teaching learning based optimization) to identify parameters of solar cell models. GOTLBO employs generalized opposition-based learning to basic teaching learning based optimization through the initialization step and generation jumping so that the convergence speed is enhanced. The performance of GOTLBO is comprehensively evaluated in thirteen benchmark functions and two parameter identification problems of solar cell models, i.e., single diode model and double diode model. Simulation results indicate the excellent performance of GOTLBO compared with four well-known evolutionary algorithms and other parameter extraction techniques proposed in the literature.