Estimation of Uncertain Parameters in Single and Double Diode Models of Photovoltaic Panels Using Frilled Lizard Optimization

Renewable energy sources are increasingly crucial for sustainable development. Photovoltaic (PV) systems, which convert solar energy into electricity, offer an environmentally friendly solution. Enhancing energy efficiency and minimizing environmental impacts in these systems heavily rely on parameter optimization. In this study, the Frilled Lizard Optimization (FLO) algorithm is proposed as a novel approach, integrating the newton-raphson method into the root mean square error (RMSE) objective function process to address nonlinear equations. Extensive analyses conducted on RTC France, STM6-40/36, and Photowatt PWP201 modules demonstrate the superior performance of the FLO algorithm. The RMSE values were calculated as SDM: 0.0030375 and DDM: 0.011538 for RTC France; 0.012036 for STM6-40/36; and 0.0097545 for Photowatt-PWP201, indicating significantly lower error margins compared to other optimization methods. Additionally, comprehensive evaluations were carried out using error metrics such as IAE, RE, and MAE, supported by detailed graphical representations of measured and predicted parameters. Current-voltage (I-V) and power-voltage (P-V) characteristic curves, as well as convergence behaviors, were systematically analyzed. This study introduces an innovative and robust solution for parameter optimization in PV systems, contributing to both theoretical and industrial applications.