Enhancement of Electrical Parameter Extraction from Solar Cells Using a Hybrid Genetic Algorithm with the Levenberg-Marquardt Method

Abstract

Accurate modeling and simulation of solar photovoltaic (PV) systems are critical for optimizing their performance and efficiency. This requires precise determination of electrical parameters of solar cells, such as photocurrent ( I ph ), saturation current ( I 0 ), series resistance ( R s ), shunt resistance ( R sh ), and ideality factor ( n ). Traditional numerical methods for parameter extraction often face limitations in complexity, speed, and assumption dependencies. To address these issues, this study proposes a hybrid method that combines a genetic algorithm with the Levenberg-Marquardt algorithm (GALM) for solar cell parameter extraction. The genetic algorithm provides a robust initial estimate of the parameters, which is then refined by the Levenberg-Marquardt algorithm to achieve high accuracy. The performance of the proposed GALM method is validated using experimental data from a 57-mm silicon solar cell from R.T.C. France. Results indicate that the GALM method achieves one of the lowest RMSE values compared to other optimization techniques, demonstrating its effectiveness in accurately extracting solar cell parameters and closely matching the experimental I-V data. This contributes significantly to optimizing the performance and efficiency of PV systems.