Electrical parameter estimation in solar cells using single-, double-, and three-diode models

Accurately modeling photovoltaic (PV) systems is essential for performance optimization and reliability assessment in renewable energy applications. This study proposes a novel hybrid methodology for parameter estimation in single-, double-, and three-diode PV models, which combines the Equilibrium Optimization Algorithm (EOA) with the Newton-Raphson method to solve the implicit model equations. This approach was implemented in Python and validated using experimental current-voltage (I-V) data from the Kyocera KC200GT solar module. The objective function aimed to minimize the root mean square error (RMSE) between simulated and measured curves, wherein current values were numerically computed via the Newton-Raphson method for each candidate solution. To evaluate the performance of the models, comparisons were carried out under standard testing conditions (STC) with an irradiance level of 1000 W/m². The double-diode model reported the lowest RMSE value under these conditions (RMSE = 0.0416 A), confirming its superior accuracy and adequate balance between complexity and performance. Additionally, two lower irradiance levels (800 W/m² and 400 W/m²) were analyzed in order to assess the consistency of the estimated parameters, i.e., the series resistance (Rs), shunt resistance (Rsh), and ideality factors (n₁, n₂, n₃). This extended analysis revealed that the Rsh parameter exhibits high variability among the three models, with STC showing the greatest deviation (63.28). This further supports the robustness of the proposed method, particularly in the case of the double-diode model. Overall, the hybrid EOA–Newton–Raphson strategy provides a reliable and flexible framework for nonlinear parameter identification in PV systems.