Parameter estimation of photovoltaic model via parallel particle swarm optimization algorithm

Jieming Ma*, Ka Lok Man, Sheng Uei Guan, T. O. Ting, Prudence W.H. Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


Summary Recently, bio-inspired metaheuristic algorithms have been widely used as powerful optimization tools to estimate crucial parameters of photovoltaic (PV) models. However, the computational cost involved in terms of the time increases as data size or the complexity of the applied PV electrical model increases. Hence, to overcome these limitations, this paper presents the parallel particle swarm optimization (PPSO) algorithm implemented in Open Computing Language (OpenCL) to solve the parameter estimation problem for a wide range of PV models. Experimental and simulation results demonstrate that the PPSO algorithm not only has the capability of obtaining all the parameters with extremely high accuracy but also dramatically improves the computational speed. This is possible and is shown in this work via the inherent capabilities of the parallel processing framework.

Original languageEnglish
Pages (from-to)343-352
Number of pages10
JournalInternational Journal of Energy Research
Issue number3
Publication statusPublished - 10 Mar 2016


  • modeling
  • parallel algorithms
  • parameter estimation
  • photovoltaic cells
  • solar energy

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