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PV system fuzzy logic MPPT method and PI control as a charge controller

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  • Yilmaz, Unal
  • Kircay, Ali
  • Borekci, Selim

Abstract

This paper puts forward to Fuzzy Logic MPPT (Maximum Power Point Tracking) method applied photovoltaic panel sourced boost converter, under variable temperature (25–60°C) and irradiance (700–1000W/m2) after that the PI control was applied buck converter to behave as a charge controller. The voltage and current of PV panels are nonlinear and they depend on environmental conditions such as temperature and irradiance. Variable environmental conditions cause to change voltage, current and also cause to change maximum available power of PV panels. To increase efficiency and decrease payback period of the system, it needs to operate PV panels at maximum power point (MPP). Under any environment conditions there is unique MPP. To operate PV panels at that point (MPP) there are many MPPT method in literature, FLC MPPT method was preferred in this study because, its rapid response to changing environmental conditions and not affecting by change of circuit parameters. The accuracy of FLC MPPT method used in this system to find MPP changes, from 94.8% to 99.4%. To charge a battery there are two traditional methods which are constant current (CC), and constant voltage (CV) methods. For fast charging with low loss constant current and voltage source is a need. One of the methods providing constant is PI control which used in this study. PI control is not only well developed and a simple technique but also it provides satisfactory results. The goal of this study is operating PV panel at maximum power point under variable environment conditions to increase efficiency and reduce cost and also provide appropriate current and voltage for charging battery to charge quickly, reduce losses and also increase life cycle of battery. This system was established and analyzed in MATLAB/Simulink.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:81:y:2018:i:p1:p:994-1001
    DOI: 10.1016/j.rser.2017.08.048
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    References listed on IDEAS

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    1. 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.
    2. Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
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    8. Shahzad Ahmed & Hafiz Mian Muhammad Adil & Iftikhar Ahmad & Muhammad Kashif Azeem & Zil e Huma & Safdar Abbas Khan, 2020. "Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation," Energies, MDPI, vol. 13(14), pages 1-24, July.
    9. Mohamed Yassine Allani & Jamel Riahi & Silvano Vergura & Abdelkader Mami, 2021. "FPGA-Based Controller for a Hybrid Grid-Connected PV/Wind/Battery Power System with AC Load," Energies, MDPI, vol. 14(8), pages 1-17, April.
    10. Zahra Bel Hadj Salah & Saber Krim & Mohamed Ali Hajjaji & Badr M. Alshammari & Khalid Alqunun & Ahmed Alzamil & Tawfik Guesmi, 2023. "A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System," Sustainability, MDPI, vol. 15(12), pages 1-38, June.
    11. Marwen Bjaoui & Brahim Khiari & Ridha Benadli & Mouad Memni & Anis Sellami, 2019. "Practical Implementation of the Backstepping Sliding Mode Controller MPPT for a PV-Storage Application," Energies, MDPI, vol. 12(18), pages 1-22, September.
    12. Li, Xingshuo & Wen, Huiqing & Hu, Yihua & Jiang, Lin, 2019. "A novel beta parameter based fuzzy-logic controller for photovoltaic MPPT application," Renewable Energy, Elsevier, vol. 130(C), pages 416-427.
    13. Bahrami, Milad & Gavagsaz-Ghoachani, Roghayeh & Zandi, Majid & Phattanasak, Matheepot & Maranzanaa, Gaël & Nahid-Mobarakeh, Babak & Pierfederici, Serge & Meibody-Tabar, Farid, 2019. "Hybrid maximum power point tracking algorithm with improved dynamic performance," Renewable Energy, Elsevier, vol. 130(C), pages 982-991.
    14. Petru Livinti, 2021. "Comparative Study of a Photovoltaic System Connected to a Three-Phase Grid by Using PI or Fuzzy Logic Controllers," Sustainability, MDPI, vol. 13(5), pages 1-14, February.
    15. Carlos Restrepo & Nicolas Yanẽz-Monsalvez & Catalina González-Castaño & Samir Kouro & Jose Rodriguez, 2021. "A Fast Converging Hybrid MPPT Algorithm Based on ABC and P&O Techniques for a Partially Shaded PV System," Mathematics, MDPI, vol. 9(18), pages 1-25, September.
    16. Peter Makeen & Hani A. Ghali & Saim Memon, 2022. "Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery," Sustainability, MDPI, vol. 14(12), pages 1-16, June.

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