Harnessing hybrid intelligence: Four vector metaheuristic and differential evolution for optimized photovoltaic parameter extraction

Abstract

Accurate parameter extraction in photovoltaic (PV) cells and modules is crucial for optimizing performance and ensuring efficient energy conversion in solar technologies. However, existing optimization methods exhibit inherent limitations. The Four Vector Intelligent Metaheuristic (FVIM) demonstrates strong local refinement but suffers from limited global exploration and premature convergence. Meanwhile, Differential Evolution (DE) offers effective global search but often struggles with stagnation in local optima. To overcome these challenges, we introduce a novel hybrid algorithm that synergistically combines FVIM's multi-vector refinement strategy with DE's robust mutation and crossover mechanisms. This hybridization ensures a balanced trade-off between local exploitation and global exploration, significantly reducing the Root Mean Square Error (RMSE) between measured and estimated current values, ensuring precise parameter estimation. The FVIM-DE algorithm is rigorously benchmarked against 15 state-of-the-art metaheuristic algorithms across three standard PV models: the Single Diode Model (SDM), Double Diode Model (DDM), and Photovoltaic Module Model (PMM). Additionally, it was evaluated on various PV technologies under different irradiance and temperature conditions. Additionally, it was evaluated on various PV technologies under different irradiance and temperature conditions. FVIM-DE consistently achieves the lowest RMSE values, with a minimum of 9.8602E-4 for SDM, 9.8248E-4 for DDM, and 2.4250E-3 for PMM, surpassing all competing algorithms. Furthermore, the Friedman test ranks FVIM-DE first across all PV models, highlighting its robustness and statistical superiority. Results consistently highlight FVIM-DE's superior accuracy, rapid convergence, and adaptability, outperforming other methods in minimizing RMSE. This positions FVIM-DE as a reliable and effective tool for PV parameter extraction, advancing solar energy applications under diverse environmental conditions.