FDB-Based Chimp Optimization Algorithm for Parameter Extraction of MonoCrystalline PERC Solar Photovoltaic Modules

This article aims to employ the fitness distance balance-based chimp optimization algorithm (FDBChOA), an efficient metaheuristic exploration approach that can be applied to tackle global optimization issues and optimize parameters for Mono-Crystalline PERC solar photovoltaic modules. These modules possess better energy conversion efficiency, which enables them to produce more electricity from the same quantity of sunlight. They need less installation area because of their efficiency, making them suitable for limited area rooftops. The suggested algorithm's search performance has been tested in the single and double diode models of Mono-Crystalline PERC solar photovoltaic cells. This paper considers squared errors at three critical operating points of the considered photovoltaic module as an objective for extracting parameters. The simulation results indicated that the FDBChOA algorithm achieved higher-quality solutions than the standalone ChOA algorithm for the parameter extraction problem.