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Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid

Author

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  • 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 49112, Korea)

  • Jae-Sub Ko

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

Abstract

This paper proposes the method for maximum power point tracking (MPPT) of the photovoltaic (PV) system. The conventional PI controller controls the system with fixed gains. Conventional PI controllers with fixed gains cannot satisfy both transient and steady-state. Therefore, to overcome the shortcomings of conventional PI controllers, this paper presents the variable gain proportional integral (VGPI) controllers that control the gain value of PI controllers using fuzzy control. Inputs of fuzzy control used in the VGPI controller are the slope from the voltage-power characteristics of the PV module. This paper designs fuzzy control’s membership functions and rule bases using the characteristics that the slope decreases in size, as it approaches the maximum power point and increases as it gets farther. In addition, the gain of the PI controller is adjusted to increase in transient-state and decrease in steady-state in order to improve the error in steady-state and the tracking speed of maximum power point of the PV system. The performance of the VGPI controller has experimented in cases where the solar radiation is constant and the solar radiation varies, to compare with the performance of the P&O method, which is traditionally used most often in MPPT, and the performance of the PI controller, which is used most commonly in the industry field. Finally, the results from the experiment are presented and the results are analyzed.

Suggested Citation

  • Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2019. "Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid," Sustainability, MDPI, vol. 11(21), pages 1-27, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:5891-:d:279586
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    References listed on IDEAS

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    Cited by:

    1. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    2. Fatemeh Jamshidi & Mohammad Reza Salehizadeh & Reza Yazdani & Brian Azzopardi & Vibhu Jately, 2023. "An Improved Sliding Mode Controller for MPP Tracking of Photovoltaics," Energies, MDPI, vol. 16(5), pages 1-20, March.
    3. Abdul Conteh & Mohammed Elsayed Lotfy & Oludamilare Bode Adewuyi & Paras Mandal & Hiroshi Takahashi & Tomonobu Senjyu, 2020. "Demand Response Economic Assessment with the Integration of Renewable Energy for Developing Electricity Markets," Sustainability, MDPI, vol. 12(7), pages 1-20, March.
    4. Catalina González-Castaño & James Marulanda & Carlos Restrepo & Samir Kouro & Alfonso Alzate & Jose Rodriguez, 2021. "Hardware-in-the-Loop to Test an MPPT Technique of Solar Photovoltaic System: A Support Vector Machine Approach," Sustainability, MDPI, vol. 13(6), pages 1-16, March.
    5. Andrés Tobón & Julián Peláez-Restrepo & Jhon Montano & Mariana Durango & Jorge Herrera & Asier Ibeas, 2020. "MPPT of a Photovoltaic Panels Array with Partial Shading Using the IPSM with Implementation Both in Simulation as in Hardware," Energies, MDPI, vol. 13(4), pages 1-17, February.
    6. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.

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