Optimisation of solar photovoltaic (PV) parameters using meta-heuristics

Valentine Obiora*, Chitta Saha, Ammar Al Bazi, Koushik Guha

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper presents a critical analysis of the meta-heuristic techniques used in various researches on the optimisation of photovoltaic (PV) parameters, which involves the use of different algorithms in order to extract and improve these parameters from the single diode model (SDM), double diode model (DDM) and three diode model (TDM) respectively. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will also equate the results of datasheet values from PV manufactures with experiment values obtained from PV module measurements. The meta-heuristics techniques to be considered include genetic algorithm (GA), particle swarm optimisation (PSO), harmony search (HS), flower pollination algorithm (FPA), simulated annealing (SA), teaching learning based optimisation (TLBO), and other different hybrid solutions to optimize the convergence speed. Root mean square error (RMSE) is used as a performance indicator of each meta-heuristic technique. These optimisation techniques are utilised in extracting the parameters of a 5 W polycrystalline panel at standard testing conditions. The results presented in this paper compared the performances of the mentioned meta-heuristics on the single, double and triple diode models respectively.

Original languageEnglish
Pages (from-to)3161-3169
Number of pages9
JournalMicrosystem Technologies
Issue number8
Publication statusPublished - 28 Oct 2020

Bibliographical note

Funding Information:
This publication was supported financially by the Institute of Future Cities and Transport, Coventry University.

Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00542-020-05066-3


  • double diode model
  • genetic algorithms
  • single diode model
  • parameter extraction
  • photovoltaic cell models
  • three diode model


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