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A Fuzzy Logical-Based Variable Step Size P&O MPPT Algorithm for Photovoltaic System

Author

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  • John Macaulay

    (College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, UK)

  • Zhongfu Zhou

    (College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, UK)

Abstract

This paper presents a Modified Perturb & Observe (P&O) Maximum power point tracking (MPPT) algorithm using fuzzy logic-based variable step size to overcome some of the limitations associated with the conventional P&O MPPT tracking method to improve the transient response and reduce the steady-state terminal voltage oscillations. The proposed MPPT algorithm was implemented and tested on an indoor emulated PV source that is constructed from a conventional solar panel and a DC power supply, a boost DC-DC converter and a dSPACE-based MPPT controller. The advantage of implementing this testing platform for MPPT is easy implementation and indoor testing of MPPT algorithms and DC-DC power converters. Thus, dependency on atmospheric conditions such as irradiance level can be avoided. Details of the emulated PV source mathematical model and electrical characteristics, the proposed MPPT algorithm via dSPACE, simulation and test results were presented in the paper.

Suggested Citation

  • John Macaulay & Zhongfu Zhou, 2018. "A Fuzzy Logical-Based Variable Step Size P&O MPPT Algorithm for Photovoltaic System," Energies, MDPI, vol. 11(6), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1340-:d:148917
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    References listed on IDEAS

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    5. Punitha, K. & Devaraj, D. & Sakthivel, S., 2013. "Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions," Energy, Elsevier, vol. 62(C), pages 330-340.
    6. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
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    Cited by:

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    2. Md Tahmid Hussain & Adil Sarwar & Mohd Tariq & Shabana Urooj & Amal BaQais & Md. Alamgir Hossain, 2023. "An Evaluation of ANN Algorithm Performance for MPPT Energy Harvesting in Solar PV Systems," Sustainability, MDPI, vol. 15(14), pages 1-36, July.
    3. Aryuanto Soetedjo & Irrine Budi Sulistiawati, 2020. "Implementing Discrete Model of Photovoltaic System on the Embedded Platform for Real-Time Simulation," Energies, MDPI, vol. 13(17), pages 1-22, August.
    4. Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    5. Grażyna Frydrychowicz-Jastrzębska & Artur Bugała, 2021. "Solar Tracking System with New Hybrid Control in Energy Production Optimization from Photovoltaic Conversion for Polish Climatic Conditions," Energies, MDPI, vol. 14(10), pages 1-26, May.
    6. Jose Miguel Espi & Jaime Castello, 2019. "A Novel Fast MPPT Strategy for High Efficiency PV Battery Chargers," Energies, MDPI, vol. 12(6), pages 1-16, March.
    7. Mehmet Ali Yildirim & Marzena Nowak-Ocłoń, 2020. "Modified Maximum Power Point Tracking Algorithm under Time-Varying Solar Irradiation," Energies, MDPI, vol. 13(24), pages 1-15, December.
    8. Novie Ayub Windarko & Muhammad Nizar Habibi & Bambang Sumantri & Eka Prasetyono & Moh. Zaenal Efendi & Taufik, 2021. "A New MPPT Algorithm for Photovoltaic Power Generation under Uniform and Partial Shading Conditions," Energies, MDPI, vol. 14(2), pages 1-22, January.
    9. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2023. "A Fuzzy Logic Control for Maximum Power Point Tracking Algorithm Validated in a Commercial PV System," Energies, MDPI, vol. 16(2), pages 1-14, January.
    10. Marco Balato & Carlo Petrarca, 2020. "The Impact of Reconfiguration on the Energy Performance of the Distributed Maximum Power Point Tracking Approach in PV Plants," Energies, MDPI, vol. 13(6), pages 1-19, March.
    11. Eneko Artetxe & Jokin Uralde & Oscar Barambones & Isidro Calvo & Imanol Martin, 2023. "Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    12. Marco Balato & Annalisa Liccardo & Carlo Petrarca, 2020. "Dynamic Boost Based DMPPT Emulator," Energies, MDPI, vol. 13(11), pages 1-16, June.
    13. Jose Miguel Espi & Jaime Castello, 2019. "New Fast MPPT Method Based on a Power Slope Detector for Single Phase PV Inverters," Energies, MDPI, vol. 12(22), pages 1-20, November.
    14. Hyeon-Seok Lee & Jae-Jung Yun, 2019. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-17, September.
    15. Ehsan Norouzzadeh & Ahmad Ale Ahmad & Meysam Saeedian & Gholamreza Eini & Edris Pouresmaeil, 2019. "Design and Implementation of a New Algorithm for Enhancing MPPT Performance in Solar Cells," Energies, MDPI, vol. 12(3), pages 1-17, February.

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