A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization

H.G.G. Nunes, J.A.N. Pombo, S.J.P.S. Mariano, M.R.A. Calado and J.A.M. Felippe de Souza

Applied Energy, 2018, vol. 211, issue C, 774-791

Abstract: Determining the mathematical model parameters of photovoltaic (PV) cells and modules represents a great challenge. In the last few years, several analytical, numerical and hybrid methods have been proposed for extracting the PV model parameters from datasheets provided by the manufacturers or from experimental data, although it is difficult to determine highly reliable solutions quickly and accurately. In this paper, we propose a new method for determining the PV parameters of both the single-diode and the double-diode models, based on the guaranteed convergence particle swarm optimization (GCPSO), using experimental data under different operating conditions. The main advantage of this method is its ability to avoid premature convergence in the optimization of complex and multimodal objective functions, such as the function that determines PV parameters. To validate performance, the GCPSO method was compared with several analytical, numerical and hybrid methods found in the literature. This validation considered three different case studies. The first two are important reference case studies in the literature and have been widely used by researchers. The third was performed in an experimental environment, in order to test the proposed method under a real implementation. The proposed methodology can find highly accurate solutions while demanding a reduced computational cost. Comparisons with other published methods demonstrate that the proposed method produces very good results in the extraction of the PV model parameters.

Keywords: Guaranteed convergence particle swarm optimization; Parameter extraction; Single-diode model; Double-diode model; Experimental data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (23)

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DOI: 10.1016/j.apenergy.2017.11.078

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