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.