Parameters identification for a photovoltaic module: comparison between PSO, GA and CS metaheuristic optimisation algorithms
by Ines Ben Ali; Mohamed Wissem Naouar; Eric Monmasson
International Journal of Modelling, Identification and Control (IJMIC), Vol. 38, No. 3/4, 2021

Abstract: In the last decade, metaheuristic optimisation algorithms became widely used for parameter identification of PV cells/modules. For this purpose, this paper presents a comparative study between three metaheuristic optimisation algorithms: genetic algorithm (GA), particle swarm optimisation (PSO) and cuckoo search (CS). The presented process for PV-cell parameters estimation is particularly based on: (i) a simplified analytical model of a PV cell; and (ii) only three I-V curve points that are always available from technical data. The comparative study showed that GA is not appropriate to be used in parameter estimation of the PV model. The CS algorithm exhibited its efficiency over other algorithms in terms of estimation accuracy and easiness of implementation. However, PSO and particularly the hybrid PSO combined with Pattern Search algorithm (PSO-PS) appeared to be the most promising in terms of computational efficiency by offering faster speed convergence to the global optimum solution with few tuned parameters.

Online publication date: Mon, 13-Jun-2022

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