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

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

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.

Suggested Citation

  • Abbassi, Abdelkader & Abbassi, Rabeh & Heidari, Ali Asghar & Oliva, Diego & Chen, Huiling & Habib, Arslan & Jemli, Mohamed & Wang, Mingjing, 2020. "Parameters identification of photovoltaic cell models using enhanced exploratory salp chains-based approach," Energy, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304400
    DOI: 10.1016/j.energy.2020.117333
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