Exploring the potential of hybridized metaheuristic algorithms for photovoltaic model parameter estimation

Proper modeling of photovoltaic (PV) cells is a highly non-linear and multidimensional problem whose optimization using standalone meta-heuristic techniques faces premature convergence due to the large number of parameters. This paper seeks to fill in a major gap in the literature through a permutation study of Particle Swarm Optimization (PSO), Differential Evolution (DE), Simulated Annealing (SA) and Nelder-Mead (NM) simplex method to determine the optimal "hand-off" scheme for balancing exploration and exploitation. The proposed approach has been evaluated using SDM, DDM, and TDM using R.T.C. France silicon cell, Photowatt-PWP 201 module and an independent set of data on flexible dual junction amorphous silicon cell. To ensure state-of-the-art robustness of the hybrid approach, it was also benchmarked against CEC 2017 test suite. Statistical analysis from 30 runs has been performed using Wilcoxon signed-rank test ( p<1.86× 10⁻⁹ ), demonstrate that a sequential "Global-to-Local" transition (PSO–NM) achieves superior precision and stability, yielding a mean RMSE of 9.32 × 10- 4 for the SDM and 1.16 × 10- 3 A for the 9-parameter TDM, with coefficients of determination ( R² ) exceeding 0.9999. Complexity analysis via the Number of Function Evaluations (NFE) shows that this hybrid method is computationally worth the effort since it produces results of great decimal accuracy and at the same time remains consistent in physical parameters. This research establishes a well-founded model for high accuracy photovoltaic simulation crucial for smart energy management and diagnosis.