A reliable optimization framework for parameter identification of single‐diode solar photovoltaic model using weighted velocity‐guided grey wolf optimization algorithm and Lambert‐W function

In estimating the parameters of the five unknown parameters Single‐Diode Model (SDM) of the solar photovoltaic (PV) model, a non‐linear equation for the PV cell current is typically utilized. Then, the error between the estimated current and measured current is computed using the objective function called Root‐Mean‐Square‐Error (RMSE). In order to compute the PV cell current in SDM, an iterative method built on the Lambert‐W function is presented in this study. Along with the Lamber‐W function, an optimization algorithm called Weighted Velocity‐Guided Grey Wolf Optimizer (WVGGWO) is used to identify the unknown lumped parameters of SDM of the cell and the module. The proposed WVGGWO is an updated version of the original Grey Wolf Optimizer (GWO). The position update of the GWO has been modified, and the weightage has been provided for the wolf hierarchy. Additionally, by emphasizing the lengthening of each leading wolf's steps towards the others in the earlier search while emphasizing the shortening of the steps while reaching the later iterations, WVGGWO improves both the exploration and exploitation of the original GWO. Four case studies are considered for testing the validity of the proposed algorithm along with the Lambert‐W function. The performance of the proposed approach is compared with seven other well‐known algorithms. The results demonstrate that the suggested approach produces better outcomes than many optimization algorithms. The balance between the exploration and exploitation phases is well established in a new WVGGWO to estimate the best values of the unknown PV variables. The objective function is optimized by the hybrid Lambert‐W function and metaheuristic algorithm. Intensive validations are made through comparisons to seven other algorithms and by using a series of experimental data.