Optimal Parameter Extraction of Three-Diode Photovoltaic Model Using the Hybrid Golden Jackal Optimizer With Fitness Distance Balance Mechanism and Berndt-Hall-Hall-Hausman Method

Accurate simulation and operation of photovoltaic (PV) systems depend on reliable extraction of model parameters from experimental data. These parameters are vital in assessing system efficiency under different environmental conditions. Due to the nonlinear characteristics of PV systems, robust optimization algorithms are necessary to ensure precise parameter estimation. This study introduces the Golden Jackal Optimization with dynamic Fitness Distance Balance (GJO-dFDB) algorithm in combination with the Berndt-Hall-Hall-Hausman (BHHH) method for estimating parameters of the three-diode PV model, which is widely observed as a benchmark for representing PV cell behavior. Integrating the fitness distance balance principle into the GJO framework strengthens its search capability by maintaining a dynamic balance between exploration and exploitation. This framework reduces the likelihood of premature convergence and improves adaptability across varying search landscapes. The performance of the proposed GJO-dFDB algorithm is compared with seven state-of-the-art optimization techniques on a commercial PV module under diverse operating conditions. The statistical results highlight its superiority, with average values of RMSE, MBE, R², RE, AE, and RT recorded as 3.675E−04, 5.789E−12, 0.9998, 3.340E−07, 1.483E−07, and 14.430, respectively. These findings confirm the GJO-dFDB algorithm's ability to achieve a trade-off between accuracy and computational efficiency in PV parameter estimation.