Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach

Abdelkader Abbassi, Rabeh Abbassi, Ali Asghar Heidari, Diego Oliva, Huiling Chen, Arslan Habib, Mohamed Jemli and Mingjing Wang

Energy, 2020, vol. 198, issue C

Abstract: The integration of photovoltaic systems (PVSs) in future power systems grows into a more attractive choice. Thus, the studies related to PVSs operation have gained immense interest. Particularly, research in identifying PV cell model parameters remains an agile field because of the non-linearity of PV cell characteristics and its wide dependency on meteorological conditions of irradiation level and temperature. This paper proposes an Opposition-based Learning Modified Salp Swarm Algorithm (OLMSSA) for accurate identification of the two-diode model parameters of the electrical equivalent circuit of the PV cell/module. Six metaheuristic algorithms, including the recently released basic algorithm SSA, used with the benchmark test PV model of the double diode, and a practical PV module, are employed to assess the performance of OLMSSA. The experimental results and the in-depth comparative study clearly demonstrate that OLMSSA is highly competitive and even significantly better than the reported results of the majority of recently-developed parameter identification methods.

Keywords: Photovoltaic panels; I–V characteristics; Parameters extraction; Two-diode model; Metaheuristic optimizer; Salp swarm algorithm (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304400

DOI: 10.1016/j.energy.2020.117333

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