Parameters optimization of solar PV cell using genetic-iterative algorithm

Abstract

Accurately determining optimal parameters from current-voltage (IV) data in solar photovoltaic (PV) models is crucial for effective system simulation and control. In this study, we propose a novel approach that combines genetic algorithm and iterative techniques maximizing their strengths, and exploiting the influence of each parameter on the IV curve to categorize them into groups. This adaptable method can adjust the interval of each parameter to different scenarios during optimization. We evaluated the method across various solar cell models including both the ‘SDM’ and ‘SDM-based PMM’, achieving notable accuracy and reliability compared to other advanced meta-heuristic algorithms. The results indicate a value of 7.3870e-5 for the SDM and 9.3365e-4 for the PMM (STM6-40/36). The proposed algorithm demonstrates notable accuracy and reliability, highlighting its usefulness in accurately determining parameters in solar PV models.