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Maximum Power Point Tracking Technology of Photovoltaic Array under Partial Shading Based On Adaptive Improved Differential Evolution Algorithm

Author

Listed:
  • Peng Zhang

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China)

  • Huibin Sui

    (Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China)

Abstract

In the case of partial shading conditions, there will be more than one maximum power point (MPP) in photovoltaic (PV) array. The traditional maximum power point tracking (MPPT) methods are easy to get in the local maximum power point (LMPP) and fail. Based on the standard differential evolution (DE) algorithm, the mutation strategy, scaling factor F, and cross factor CR of the algorithm are optimized. Also, the population position variance δ 2 is used to prevent falling into LMPP. Finally, the conditions for algorithm termination and restart are set. It is verified by simulation that the method has fast convergence speed, high accuracy, and can adapt well to changes in the external environment. The improved DE algorithm has a great advantage in MPPT.

Suggested Citation

  • Peng Zhang & Huibin Sui, 2020. "Maximum Power Point Tracking Technology of Photovoltaic Array under Partial Shading Based On Adaptive Improved Differential Evolution Algorithm," Energies, MDPI, vol. 13(5), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1254-:d:329917
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    References listed on IDEAS

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    1. Hosenuzzaman, M. & Rahim, N.A. & Selvaraj, J. & Hasanuzzaman, M. & Malek, A.B.M.A. & Nahar, A., 2015. "Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 284-297.
    2. Jirada Gosumbonggot & Goro Fujita, 2019. "Partial Shading Detection and Global Maximum Power Point Tracking Algorithm for Photovoltaic with the Variation of Irradiation and Temperature," Energies, MDPI, vol. 12(2), pages 1-22, January.
    3. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
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    Cited by:

    1. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.
    2. Tiago H. de A. Mateus & José A. Pomilio & Ruben B. Godoy & João O. P. Pinto, 2022. "VSG Control Applied to Seven-Level PV Inverter for Partial Shading Impact Abatement," Energies, MDPI, vol. 15(17), pages 1-14, September.
    3. Muhammad Mateen Afzal Awan & Aamer Bilal Asghar & Muhammad Yaqoob Javed & Zsolt Conka, 2023. "Ordering Technique for the Maximum Power Point Tracking of an Islanded Solar Photovoltaic System," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    4. Muhammad Mateen Afzal Awan & Muhammad Yaqoob Javed & Aamer Bilal Asghar & Krzysztof Ejsmont, 2022. "Performance Optimization of a Ten Check MPPT Algorithm for an Off-Grid Solar Photovoltaic System," Energies, MDPI, vol. 15(6), pages 1-31, March.

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