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Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization

Author

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  • Huawen Sheng

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

  • Chunquan Li

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

  • Hanming Wang

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

  • Zeyuan Yan

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

  • Yin Xiong

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

  • Zhenting Cao

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

  • Qianying Kuang

    (School of Information Engineering, Nanchang University, Nanchang 330029, China)

Abstract

Photovoltaic (PV) models’ parameter extraction with the tested current-voltage values is vital for the optimization, control, and evaluation of the PV systems. To reliably and accurately extract their parameters, this paper presents one improved moths-flames optimization (IMFO) method. In the IMFO, a double flames generation (DFG) strategy is proposed to generate two different types of target flames for guiding the flying of moths. Furthermore, two different update strategies are developed for updating the positions of moths. To greatly balance the exploitation and exploration, we adopt a probability to rationally select one of the two update strategies for each moth at each iteration. The proposed IMFO is used to distinguish the parameter of three test PV models including single diode model (SDM), double diode model (DDM), and PV module model (PMM). The results indicate that, compared with other well-established methods, the proposed IMFO can obtain an extremely promising performance.

Suggested Citation

  • Huawen Sheng & Chunquan Li & Hanming Wang & Zeyuan Yan & Yin Xiong & Zhenting Cao & Qianying Kuang, 2019. "Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization," Energies, MDPI, vol. 12(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3527-:d:267072
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    References listed on IDEAS

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    1. 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.
    2. Patel, Sanjaykumar J. & Panchal, Ashish K. & Kheraj, Vipul, 2014. "Extraction of solar cell parameters from a single current–voltage characteristic using teaching learning based optimization algorithm," Applied Energy, Elsevier, vol. 119(C), pages 384-393.
    3. 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.
    4. Bastidas-Rodriguez, J.D. & Petrone, G. & Ramos-Paja, C.A. & Spagnuolo, G., 2017. "A genetic algorithm for identifying the single diode model parameters of a photovoltaic panel," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 38-54.
    5. 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.
    6. 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.
    7. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
    8. Lau, Lee Chung & Lee, Keat Teong & Mohamed, Abdul Rahman, 2012. "Global warming mitigation and renewable energy policy development from the Kyoto Protocol to the Copenhagen Accord—A comment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 5280-5284.
    9. 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.
    10. Jieming Ma & T. O. Ting & Ka Lok Man & Nan Zhang & Sheng-Uei Guan & Prudence W. H. Wong, 2013. "Parameter Estimation of Photovoltaic Models via Cuckoo Search," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, August.
    11. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    12. Fathy, Ahmed & Rezk, Hegazy, 2017. "Parameter estimation of photovoltaic system using imperialist competitive algorithm," Renewable Energy, Elsevier, vol. 111(C), pages 307-320.
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    5. Zhang, Yiying & Ma, Maode & Jin, Zhigang, 2020. "Comprehensive learning Jaya algorithm for parameter extraction of photovoltaic models," Energy, Elsevier, vol. 211(C).
    6. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.
    7. Abd-ElHady Ramadan & Salah Kamel & Tahir Khurshaid & Seung-Ryle Oh & Sang-Bong Rhee, 2021. "Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    8. Chennoufi, Khalid & Ferfra, Mohammed & Mokhlis, Mohcine, 2021. "An accurate modelling of Photovoltaic modules based on two-diode model," Renewable Energy, Elsevier, vol. 167(C), pages 294-305.
    9. Rizk-Allah, Rizk M. & El-Fergany, Attia A., 2021. "Emended heap-based optimizer for characterizing performance of industrial solar generating units using triple-diode model," Energy, Elsevier, vol. 237(C).
    10. Mehmet Yesilbudak, 2021. "Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy," Energies, MDPI, vol. 14(18), pages 1-27, September.
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    13. Li, Shuijia & Gong, Wenyin & Gu, Qiong, 2021. "A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).

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