Advancements in solar photovoltaic modelling: selective opposition-based variable weighted grey wolf optimizer with improved Newton–Raphson analysis

This paper proposes a unique method for estimating three-diode photovoltaic (PV) model parameters that uses an enhanced Newton–Raphson (NR) method and the selective opposition-based grey wolf optimization (GWO) algorithm with variable weights. For PV systems to operate more effectively, it is essential that these characteristics be estimated accurately. The GWO algorithm and the NR method are used to their full potential in the suggested method to overcome the drawbacks of conventional approaches. The selective opposition mechanism improves the GWO algorithm's exploration and exploitation capabilities, making it possible to search the parameter space efficiently. Using variable weights modifies the impact of various search operators, enhancing convergence rate and solution quality. An enhanced NR (ENR) approach is added to the optimization process to increase accuracy further. The ENR technique is used to revise estimated parameter values from initial guesses iteratively. A real-world PV system dataset used in experiments shows that the suggested solution performs better than state-of-the-art methods. The outcomes demonstrate better robustness, convergence speed, and accuracy. The suggested method provides an effective and efficient method for extracting the unknown parameters of the three-diode photovoltaic model. The research findings can help with photovoltaic energy generation system design and optimization.