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A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models

Author

Listed:
  • Tong Kang

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Jiangang Yao

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Min Jin

    (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Shengjie Yang

    (College of Computer and Information Engineering, Hunan University of Commerce, Changsha 410205, China)

  • ThanhLong Duong

    (Department of Electrical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam)

Abstract

Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed for solving this PV model parameter estimation problem, it is still challenging to determine highly accurate and reliable solutions. In this paper, this problem is firstly transformed into an optimization problem, and an objective function (OF) is formulated to quantify the overall difference between the experimental and simulated current data. And then, to enhance the performance of original cuckoo search algorithm (CSA), a novel improved cuckoo search algorithm (ImCSA) is proposed, by combining three strategies with CSA. In ImCSA, a quasi-opposition based learning (QOBL) scheme is employed in the population initialization step of CSA. Moreover, a dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original CSA. A dynamic adjustment mechanism for the fraction probability ( P a ) is proposed to achieve better tradeoff between the exploration and exploitation to increase searching ability. Afterwards, the proposed ImCSA is used for solving the problem of estimating parameters of PV models based on experimental I-V data. Finally, the proposed ImCSA has been demonstrated on the parameter identification of various PV models, i.e., single diode model (SDM), double diode model (DDM) and PV module model (PMM). Experimental results indicate that the proposed ImCSA outperforms the original CSA and its superior performance in comparison with other state-of-the-art algorithms, and they also show that our proposed ImCSA is capable of finding the best values of parameters for the PV models in such effective way for giving the best possible approximation to the experimental I-V data of real PV cells and modules. Therefore, the proposed ImCSA can be considered as a promising alternative to accurately and reliably estimate parameters of PV models.

Suggested Citation

  • Tong Kang & Jiangang Yao & Min Jin & Shengjie Yang & ThanhLong Duong, 2018. "A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models," Energies, MDPI, vol. 11(5), pages 1-31, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1060-:d:143252
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    References listed on IDEAS

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    1. Rongjie Wang & Yiju Zhan & Haifeng Zhou, 2015. "Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells," Energies, MDPI, vol. 8(8), pages 1-19, July.
    2. Tong, Nhan Thanh & Pora, Wanchalerm, 2016. "A parameter extraction technique exploiting intrinsic properties of solar cells," Applied Energy, Elsevier, vol. 176(C), pages 104-115.
    3. Diego Oliva & Ahmed A. Ewees & Mohamed Abd El Aziz & Aboul Ella Hassanien & Marco Peréz-Cisneros, 2017. "A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells," Energies, MDPI, vol. 10(7), pages 1-19, June.
    4. 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.
    5. 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.
    6. 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.
    7. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    8. Tong Kang & Jiangang Yao & ThanhLong Duong & Shengjie Yang & Xiangqian Zhu, 2017. "A Hybrid Approach for Power System Security Enhancement via Optimal Installation of Flexible AC Transmission System (FACTS) Devices," Energies, MDPI, vol. 10(9), pages 1-32, September.
    9. 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.
    10. Chen, Zhicong & Wu, Lijun & Lin, Peijie & Wu, Yue & Cheng, Shuying, 2016. "Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy," Applied Energy, Elsevier, vol. 182(C), pages 47-57.
    11. 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.
    12. 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.
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