Solar energy applications and research are becoming increasingly popular, and photovoltaics (PVs) are among the most significant solar energy applications. To simulate and optimise PV system performance, the optimal p...
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Solar energy applications and research are becoming increasingly popular, and photovoltaics (PVs) are among the most significant solar energy applications. To simulate and optimise PV system performance, the optimal parameters of the solar cell models should be estimated exactly. In this study, improved simplified swarm optimisation (isso), a recently introduced soft computing method based on simplified swarm optimisation, is proposed to minimise the least square error between the extracted and the measured data for the solar cell models parameter estimation of the single- and double-diode model problems. Based on the new all-variable difference update mechanism and survival of the fittest policy, the proposed algorithm is able to find an improved approximation for estimating the parameters of single- and double-diode solar cell models. As evidence of the utility of the proposed isso, the authors present extensive computational results for two benchmark problems. The comparison of the computational results supports the proposed isso algorithm outperforms the previously developed algorithms for all of the experiments in the literature.
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