Optimized Parameter Extraction in Triple Diode Solar PV Models Using Kookaburra‐based Dwarf Mongoose Approach

ABSTRACT Improving photovoltaics (PV) system performance through simulation requires accurate PV models. The nonlinear relationship between current and voltage, coupled with incomplete manufacturer data, presents a significant challenge in parameter estimation. This research work presents an innovative optimization framework designed to extract the parameters of a triple‐diode model representing an unknown Solar PV module. The proposed approach leverages a novel hybrid optimization technique called the Kookaburra‐based Dwarf Mongoose (KO‐DM) Optimization Algorithm. It determines the unknown parameter that helps to characterize the equivalent circuit of the PV cell described by the triple‐diode model. The performance of the proposed KO‐DM algorithm was assessed using two distinct datasets: (1) 25°C with 1000 W/m² and (2) 45°C with 1000 W/m². The proposed method offers superior performance, exhibiting the ability to efficiently solve complex nonlinear optimization problems, converge rapidly to the global optimum, and involve a simple computational procedure.