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

Xu Chen, Kunjie Yu, Wenli Du, Wenxiang Zhao and Guohai Liu

Energy, 2016, vol. 99, issue C, 170-180

Abstract: 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.

Keywords: Solar cell models; Parameter identification; Teaching learning based optimization; Generalized ​opposition-based learning (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (45)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:99:y:2016:i:c:p:170-180

DOI: 10.1016/j.energy.2016.01.052

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