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Comparative Assessment between Five Control Techniques to Optimize the Maximum Power Point Tracking Procedure for PV Systems

Author

Listed:
  • Fathi Troudi

    (Laboratory LAPER, University Tunis El Manar, Tunis 2092, Tunisia)

  • Houda Jouini

    (Laboratory LAPER, University Tunis El Manar, Tunis 2092, Tunisia)

  • Abdelkader Mami

    (Laboratory LAPER, University Tunis El Manar, Tunis 2092, Tunisia)

  • Nidhal Ben Khedher

    (Department of Mechanical Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia
    Laboratory of Thermal and Energetic Systems Studies, National School of Engineering of Monastir, University of Monastir, Monastir 5000, Tunisia)

  • Walid Aich

    (Department of Mechanical Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia)

  • Attia Boudjemline

    (Department of Industrial Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia)

  • Mohamed Boujelbene

    (Department of Industrial Engineering, College of Engineering, Hail University, Hail 55476, Saudi Arabia)

Abstract

Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving parts and have a long lifespan. Nevertheless, solar photovoltaic (PV) systems provide intermittent output electricity with a nonlinear output voltage. Due to this intermittent availability, PV installations are facing significant challenges. As a result, in PV power systems, a Maximum Power Point Tracker (MPPT), a power extraction mechanism, is required to assure maximum power delivery at any given moment. The main objective of this work is to study the MPPT method of extracting the maximum power from photovoltaic modules under different solar irradiation and temperatures. Several MPPT methods have been developed for photovoltaic systems to achieve MPP, depending on weather conditions and applications, ranging from simple to more complex methods. Among these methods, five techniques have been presented and compared that are P&O perturbation and observation method, INC incremental conductance method, the ANN neural network method, the open circuit voltage based neural network method FVCO, and the neural network method at the base of FCC (short circuit current).

Suggested Citation

  • Fathi Troudi & Houda Jouini & Abdelkader Mami & Nidhal Ben Khedher & Walid Aich & Attia Boudjemline & Mohamed Boujelbene, 2022. "Comparative Assessment between Five Control Techniques to Optimize the Maximum Power Point Tracking Procedure for PV Systems," Mathematics, MDPI, vol. 10(7), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1080-:d:781078
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    References listed on IDEAS

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    1. Muqaddas Elahi & Hafiz Muhammad Ashraf & Chul-Hwan Kim, 2022. "An Improved Partial Shading Detection Strategy Based on Chimp Optimization Algorithm to Find Global Maximum Power Point of Solar Array System," Energies, MDPI, vol. 15(4), pages 1-26, February.
    2. Waithiru Charles Lawrence Kamuyu & Jong Rok Lim & Chang Sub Won & Hyung Keun Ahn, 2018. "Prediction Model of Photovoltaic Module Temperature for Power Performance of Floating PVs," Energies, MDPI, vol. 11(2), pages 1-13, February.
    3. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    4. Srinivasan Vadivel & Boopathi C. Sengodan & Sridhar Ramasamy & Mominul Ahsan & Julfikar Haider & Eduardo M. G. Rodrigues, 2022. "Social Grouping Algorithm Aided Maximum Power Point Tracking Scheme for Partial Shaded Photovoltaic Array," Energies, MDPI, vol. 15(6), pages 1-17, March.
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    Cited by:

    1. 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.
    2. Prasannaa Poongavanam & Aneesh A. Chand & Van Ba Tai & Yash Munnalal Gupta & Madhan Kuppusamy & Joshuva Arockia Dhanraj & Karthikeyan Velmurugan & Rajasekar Rajagopal & Tholkappiyan Ramachandran & Kus, 2023. "Annual Thermal Management of the Photovoltaic Module to Enhance Electrical Power and Efficiency Using Heat Batteries," Energies, MDPI, vol. 16(10), pages 1-18, May.
    3. Yang Meng & Zunliang Chen & Hui Cheng & Enpu Wang & Baohua Tan, 2023. "An Efficient Variable Step Solar Maximum Power Point Tracking Algorithm," Energies, MDPI, vol. 16(3), pages 1-20, January.
    4. Zixia Yuan & Guojiang Xiong & Xiaofan Fu, 2022. "Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey," Energies, MDPI, vol. 15(22), pages 1-18, November.
    5. Ernesto Bárcenas-Bárcenas & Diego R. Espinoza-Trejo & José A. Pecina-Sánchez & Héctor A. Álvarez-Macías & Isaac Compeán-Martínez & Ángel A. Vértiz-Hernández, 2023. "An improved Fractional MPPT Method by Using a Small Circle Approximation of the P–V Characteristic Curve," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

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