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, Mingjing Wang

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


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.

Original languageEnglish
Article number117333
Publication statusPublished - 1 May 2020
Externally publishedYes


  • I–V characteristics
  • Metaheuristic optimizer
  • Parameters extraction
  • Photovoltaic panels
  • Salp swarm algorithm
  • Two-diode model

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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