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A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module

Author

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  • Yu, Kunjie
  • Qu, Boyang
  • Yue, Caitong
  • Ge, Shilei
  • Chen, Xu
  • Liang, Jing

Abstract

In order to carry out the evaluation, control and maximum power point tracking on photovoltaic (PV) systems, accurate and reliable model parameter identification of PV cell and module is always desired. In this study, a performance-guided JAYA (PGJAYA) algorithm is proposed for extracting parameters of different PV models. In proposed PGJAYA algorithm, the individual performance in the whole population is quantified through probability. Then, based on probability, each individual can self-adaptively select different evolution strategies designed for balancing exploration and exploitation abilities to conduct the searching process. Meanwhile, the quantified performance is employed to select the exemplar to construct the promising searching direction. In addition, a self-adaptive chaotic perturbation mechanism is introduced around the current best solution to explore more better solution for replacing the worst one, thus improving the quality of whole population. The parameters estimation performance of PGJAYA is evaluated through three widely used standard datasets of different PV models including single diode, double diode, and PV module. Comparative and statistical results demonstrate that PGJAYA has a superior performance as it always obtains the most accurate parameters with strong robustness among all compared algorithms. Furthermore, the tests based on experimental data from the data sheet of different types of PV modules suggest that the proposed algorithm can achieve superior results at different irradiance and temperature. Based on these superiorities, it is concluded that PGJAYA is a promising parameter identification method for PV cell and module model.

Suggested Citation

  • Yu, Kunjie & Qu, Boyang & Yue, Caitong & Ge, Shilei & Chen, Xu & Liang, Jing, 2019. "A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module," Applied Energy, Elsevier, vol. 237(C), pages 241-257.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:241-257
    DOI: 10.1016/j.apenergy.2019.01.008
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    1. Chen, Xu & Xu, Bin & Mei, Congli & Ding, Yuhan & Li, Kangji, 2018. "Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation," Applied Energy, Elsevier, vol. 212(C), pages 1578-1588.
    2. Rao, R. Venkata & Saroj, Ankit, 2017. "Constrained economic optimization of shell-and-tube heat exchangers using elitist-Jaya algorithm," Energy, Elsevier, vol. 128(C), pages 785-800.
    3. Wang, Xiaoyu & Luo, Dongkun & Zhao, Xu & Sun, Zhu, 2018. "Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation," Energy, Elsevier, vol. 152(C), pages 539-548.
    4. Olivella-Rosell, Pol & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Sumper, Andreas & Ottesen, Stig Ødegaard & Vidal-Clos, Josep-Andreu & Villafáfila-Robles, Roberto, 2018. "Optimization problem for meeting distribution system operator requests in local flexibility markets with distributed energy resources," Applied Energy, Elsevier, vol. 210(C), pages 881-895.
    5. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    6. 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.
    7. Jiangtao Yu & Chang-Hwan Kim & Abdul Wadood & Tahir Khurshiad & Sang-Bong Rhee, 2018. "A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 11(8), pages 1-25, July.
    8. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    9. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    10. Nunes, H.G.G. & Pombo, J.A.N. & Mariano, S.J.P.S. & Calado, M.R.A. & Felippe de Souza, J.A.M., 2018. "A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization," Applied Energy, Elsevier, vol. 211(C), pages 774-791.
    11. Warid Warid & Hashim Hizam & Norman Mariun & Noor Izzri Abdul-Wahab, 2016. "Optimal Power Flow Using the Jaya Algorithm," Energies, MDPI, vol. 9(9), pages 1-18, August.
    12. Oliva, Diego & Abd El Aziz, Mohamed & Ella Hassanien, Aboul, 2017. "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Applied Energy, Elsevier, vol. 200(C), pages 141-154.
    13. Chen, Xu & Yu, Kunjie & Du, Wenli & Zhao, Wenxiang & Liu, Guohai, 2016. "Parameters identification of solar cell models using generalized oppositional teaching learning based optimization," Energy, Elsevier, vol. 99(C), pages 170-180.
    14. Kunjie Yu & Xin Wang & Zhenlei Wang, 2016. "An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 831-843, August.
    15. Fathy, Ahmed & Rezk, Hegazy, 2017. "Parameter estimation of photovoltaic system using imperialist competitive algorithm," Renewable Energy, Elsevier, vol. 111(C), pages 307-320.
    16. Chen, Xi & Yang, Hongxing, 2018. "Integrated energy performance optimization of a passively designed high-rise residential building in different climatic zones of China," Applied Energy, Elsevier, vol. 215(C), pages 145-158.
    17. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.
    Full references (including those not matched with items on IDEAS)

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