Photovoltaic module characterization relies on extracting five key parameters from the single-diode equivalent-circuit model, a fundamental task in solar energy engineering. However, practitioners face a significant obstacle: the diode saturation current (I₀) spans nine orders of magnitude, creating numerical instability when optimization algorithms search this parameter on a linear scale. This wide dynamic range frequently causes gradient-free optimizers to converge to poor local solutions, undermining the accuracy of PV performance predictions critical to grid planning.
Researchers addressed this challenge by reparameterizing I₀ using a logarithmic scale (log₁₀), which uniformly maps the physically admissible saturation-current range onto a bounded interval. This transformation stabilizes the optimization landscape and improves convergence behavior. The proposed workflow combines this reparameterization with a Newton–Raphson implicit current solver and employs the Red-Tailed Hawk Algorithm (RTH) as the optimization engine. The team implemented a fixed-seed reproducibility protocol to ensure consistent, verifiable results across independent runs.
Validation occurred across three industry-standard benchmarks: the Shell SM55 monocrystalline module, the RTC France 57 mm silicon solar cell, and the Photowatt-PWP201 module. RTH was systematically compared against six competing algorithms (PSO, GWO, WOA, HHO, AVOA, and MGO) over 30 independent runs. Under a budget-neutral protocol using exactly 149,050 objective-function evaluations per run, RTH achieved the lowest median root-mean-square error (RMSE) across all three test cases, with results as low as 2.49×10⁻⁵ A on the SM55 module.
An ablation study confirmed that logarithmic I₀ sampling substantially improves run-to-run robustness, particularly for the RTC France cell, where linear sampling produced unreliable outlier results. While the authors emphasize this is a reproducible implementation framework rather than a fundamentally new PV model, the workflow provides power engineers with a reliable, standardized approach for accurate solar cell characterization—essential for accurate PV modeling in grid simulation and renewable integration studies.
