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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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    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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