Parameter Estimation of the Three-Diode Photovoltaic Model Using the Flow Direction Algorithm

Abstract

Accurate parameter identification of photovoltaic (PV) models is essential for reliable performance prediction and optimization of energy systems. However, estimating the parameters of advanced equivalent circuit models is challenging due to the nonlinear and multimodal nature of their current–voltage (I–V) characteristics. This study proposes a parameter extraction approach based on the Flow Direction Algorithm applied to the Three-Diode Model (TDM). The method minimizes the root mean square error (RMSE) between measured and simulated I–V data while ensuring physically consistent parameter sets. The approach is validated using two benchmark photovoltaic devices: the RTC France solar cell and an amorphous silicon thin-film solar cell. For the RTC France cell, the proposed method achieves an RMSE of 7.3511 × 10⁻⁴ when fitting the experimental I–V characteristics using the TDM. For the amorphous silicon cell, the algorithm obtains an RMSE of 2.2939 × 10⁻⁵, demonstrating a very close agreement between the measured and simulated electrical characteristics. These results confirm that the FDA-based optimization approach provides accurate parameter estimation for complex PV models, enabling reliable photovoltaic modeling and supporting improved performance analysis and energy system optimization.