Development of a MATLAB App designer for determining solar panel parameters using nonlinear optimization algorithm

The developed framework is focused on determining the intrinsic parameter values of a solar photovoltaic (PV)  panel, which are unavailable in the data sheet and play a very essential role in determining the panel's performance. Comprehensive mathematical equations are derived for the Single diode model (SDM) based on three distinct operating points from the manufacturer’s data sheet for a solar PV panel. To determine the intrinsic parameter values of a solar PV panel, the Newton-Rhapson (NR)-algorithm is employed in MATLAB. To justify the robustness and validate the proposed NR-algorithm prototype, an experimental dataset of two different technologies, i.e., a Radiotechnique Compelec (RTC) France monocrystalline silicon solar cell and a polycrystalline SPR6-120/36 silicon solar panel, was considered and compared with the simulated data. The proposed Newton-Raphson-based framework showed excellent agreement with experimental data. In the case of monocrystalline module, Root Mean Square Error (RMSE) was 0.00086068 and NSE was 0.9999, whereas for polycrystalline panel, RMSE was 0.016907 and Nash–Sutcliffe Efficiency (NSE) was 0.99979. These low RMSE and high NSE values show that the method's prediction accuracy and robustness are strong. This work presents a deterministic NR-based parameter estimation framework for a single-diode PV models, in which explicit nonlinear equations and physically meaningful constraints were used to ensure stable and reliable parameter extraction. A MATLAB App Designer-based implementation of the proposed methodology is also provided, which supports practical deployment and reproducibility. Accurate photovoltaic parameter estimation enables accurate prediction of solar panel energy production, enabling efficient system design and integration into the grid, increasing the share of renewable energy and reducing dependence on fossil fuels, thereby contributing to greenhouse gas emission and climate change mitigation.