Enhanced solar PV cell parameter identification via particle swarm optimization (PSO) with weighted objective function

Bikshan Ghosh; Sharmistha Mandal; Bikshan Ghosh; Sharmistha Mandal

doi:10.55981/j.mev.2025.1033

Enhanced solar PV cell parameter identification via particle swarm optimization (PSO) with weighted objective function

Bikshan Ghosh, Sharmistha Mandal

Abstract

This work aims to identify the parameters of solar photovoltaic (PV) cells, which can then be used for modeling PV systems and designing controllers. The dynamic equation governing correlation among current and voltage at the output terminals of a solar cell is predominantly dependent on different parameters of the single diode model (SDM) or double diode model (DDM) representation of that solar PV cell. Without easy access to this information, accurately modeling PV systems for further studies becomes difficult. So, to identify those parameters with greater accuracy and less complexity, particle swarm optimization (PSO) in conjunction with the weighted objective function (WOF) has been proposed in this paper. This proposition of multi-objective optimization with a metaheuristic algorithm is found to give very satisfactory results while reducing any further modification in conventional PSO and with faster convergence.

10.55981/j.mev.2025.1033

Keywords

parameter identification; solar PV cell; PSO; weighted objective function

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References

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