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Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm

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  • Jiang, Lian Lian
  • Maskell, Douglas L.
  • Patra, Jagdish C.

Abstract

A number of different analytical and numerical methods have previously been proposed to estimate solar cell/module parameters. For a single diode model, the parameters include the photocurrent, the saturation current, the parasitic series and shunt resistances, and the ideality factor. Among the proposed optimization techniques, the differential evolution (DE) based method besides its computational advantage, provides better accuracy compared to other methods. However, it is not an easy task to determine the control parameters for the DE algorithm. This paper presents an improved adaptive DE (IADE) based optimization technique. New formulas for the scaling factor and crossover rate are proposed. The proposed IADE algorithm uses a simple structure based on the feedback of fitness value in the evolutionary process. It provides better performance for estimation of the solar cell and module parameter values than other popular optimization methods such as particle swarm optimization, genetic algorithm, conventional DE, simulated annealing (SA), and a recently proposed analytical method. Comparisons with existing methods for solar cell and module parameter estimation are presented using both synthetic and experimental data under various environmental conditions. The proposed scheme offers higher accuracy, and also eliminates the need for users to manually tune the control parameters of DE algorithm.

Suggested Citation

  • Jiang, Lian Lian & Maskell, Douglas L. & Patra, Jagdish C., 2013. "Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm," Applied Energy, Elsevier, vol. 112(C), pages 185-193.
    • Handle: RePEc:eee:appene:v:112:y:2013:i:c:p:185-193
    DOI: 10.1016/j.apenergy.2013.06.004
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    References listed on IDEAS

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    1. Al-Anzi, Fawaz S. & Allahverdi, Ali, 2007. "A self-adaptive differential evolution heuristic for two-stage assembly scheduling problem to minimize maximum lateness with setup times," European Journal of Operational Research, Elsevier, vol. 182(1), pages 80-94, October.
    2. Chen, Yifeng & Wang, Xuemeng & Li, Da & Hong, Ruijiang & Shen, Hui, 2011. "Parameters extraction from commercial solar cells I-V characteristics and shunt analysis," Applied Energy, Elsevier, vol. 88(6), pages 2239-2244, June.
    3. Patra, Jagdish C. & Maskell, Douglas L., 2012. "Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks," Renewable Energy, Elsevier, vol. 44(C), pages 7-16.
    4. Ishaque, Kashif & Salam, Zainal & Mekhilef, Saad & Shamsudin, Amir, 2012. "Parameter extraction of solar photovoltaic modules using penalty-based differential evolution," Applied Energy, Elsevier, vol. 99(C), pages 297-308.
    5. AlHajri, M.F. & El-Naggar, K.M. & AlRashidi, M.R. & Al-Othman, A.K., 2012. "Optimal extraction of solar cell parameters using pattern search," Renewable Energy, Elsevier, vol. 44(C), pages 238-245.
    6. Asim, Nilofar & Sopian, Kamaruzzaman & Ahmadi, Shideh & Saeedfar, Kasra & Alghoul, M.A. & Saadatian, Omidreza & Zaidi, Saleem H., 2012. "A review on the role of materials science in solar cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5834-5847.
    7. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
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