Enhanced Parameter Extraction for Double-Diode Modeling of Solar PV Cells Using Improved Grey Wolf Optimization

Abstract Accurate pre-installation parameter assessment is essential for optimizing energy efficiency and dependability as solar energy becomes more and more relied upon as a sustainable power source to counteract the depletion of fossil fuels and environmental deterioration. Precise modeling of photovoltaic (PV) panels is essential for optimizing their performance under varying environmental conditions. Manufacturers supply important I-V characteristic data, including maximum power point, open-circuit voltage, and short-circuit current, but the Double-Diode Model (DDM) includes both diffusion and recombination mechanisms, making parameter extraction computationally demanding. An Improved Grey Wolf Optimizer (IGWO) method is proposed in this study for the precise and efficient extraction of DDM parameters from PV panel datasheets. By adding a sinusoidal curtailed function and conflict-based learning, the IGWO improves on the traditional Grey Wolf Optimizer (GWO), leading to a notable increase in convergence speed and global optimization performance. More accurate PV system modeling is made possible by the exact parameter estimate that is accomplished by applying IGWO to the DDM. This work introduces a scalable and reliable optimization framework to overcome the drawbacks of traditional extraction methods. The suggested IGWO-based method advances PV system modeling significantly and aids in the creation of sustainable and high-performing energy systems.