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Optimization Design and Test Bed of Fuzzy Control Rule Base for PV System MPPT in Micro Grid

Author

Listed:
  • Jong-Chan Kim

    (Department of Computer Engineering, Sunchon National University, 255 Jungang-ro, Suncheon-city Jeollanam do 57922, Korea)

  • Jun-Ho Huh

    (Department of Data Informatics, Korea Maritime and Ocean University, Busan 600-716, Korea)

  • Jae-Sub Ko

    (Department of Electrical Engineering, Sunchon National University, 255 Jungang-ro, Suncheon-city Jeollanam do 57922, Korea)

Abstract

This paper presents an optimal design of a fuzzy control rule base for tracking the maximum power point of a photovoltaic (PV) system. Fuzzy control is used for the maximum power point tracking (MPPT) of PV systems because it has the advantage of processing nonlinear systems. The rule base of fuzzy control depends on the user or designer’s experience and determines the fuzzy control’s performance. In this paper, we divide the MPPT state of the PV system into four cases according to the operating conditions, and propose the rule base design of the fuzzy control according to each case. The proposed method in the paper tests the MPPT performance using artificial lighting and compares the results with the conventional control method (proportional and integral (PI) and perturbation & observation (P&O) method) to prove its effectiveness.

Suggested Citation

  • Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2020. "Optimization Design and Test Bed of Fuzzy Control Rule Base for PV System MPPT in Micro Grid," Sustainability, MDPI, vol. 12(9), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3763-:d:354411
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    References listed on IDEAS

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    1. Yongli Wang & Yujing Huang & Yudong Wang & Fang Li & Yuanyuan Zhang & Chunzheng Tian, 2018. "Operation Optimization in a Smart Micro-Grid in the Presence of Distributed Generation and Demand Response," Sustainability, MDPI, vol. 10(3), pages 1-25, March.
    2. Sivakumar, P. & Abdul Kader, Abdullah & Kaliavaradhan, Yogeshraj & Arutchelvi, M., 2015. "Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions," Renewable Energy, Elsevier, vol. 81(C), pages 543-550.
    3. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. Yilmaz, Unal & Kircay, Ali & Borekci, Selim, 2018. "PV system fuzzy logic MPPT method and PI control as a charge controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 994-1001.
    5. Ben Minnaert & Peter Veelaert, 2014. "A Proposal for Typical Artificial Light Sources for the Characterization of Indoor Photovoltaic Applications," Energies, MDPI, vol. 7(3), pages 1-17, March.
    6. Hui Huang & Shilin Nie & Jin Lin & Yuanyuan Wang & Jun Dong, 2020. "Optimization of Peer-to-Peer Power Trading in a Microgrid with Distributed PV and Battery Energy Storage Systems," Sustainability, MDPI, vol. 12(3), pages 1-20, January.
    7. Min-fan He & Fu-xing Zhang & Yong Huang & Jian Chen & Jue Wang & Rui Wang, 2019. "A Distributed Demand Side Energy Management Algorithm for Smart Grid," Energies, MDPI, vol. 12(3), pages 1-19, January.
    8. Bendib, Boualem & Belmili, Hocine & Krim, Fateh, 2015. "A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 637-648.
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    Cited by:

    1. Marcus Evandro Teixeira Souza Junior & Luiz Carlos Gomes Freitas, 2022. "Power Electronics for Modern Sustainable Power Systems: Distributed Generation, Microgrids and Smart Grids—A Review," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    2. Norbert Njuanyi Koneh & Jae-Sub Ko & Dae-Kyong Kim, 2022. "Simulations to Eliminate Backflow Power in an Isolated Three-Port Bidirectional DC–DC Converter," Energies, MDPI, vol. 16(1), pages 1-14, December.

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