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Advanced MPPT Algorithm for Distributed Photovoltaic Systems

Author

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  • Hyeon-Seok Lee

    (Department of Electrical Engineering, POSTECH, Pohang 37673, Korea)

  • Jae-Jung Yun

    (Department of Electronics and Electrical Engineering, Daegu University, Gyeongsan 38453, Korea)

Abstract

The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

Suggested Citation

  • Hyeon-Seok Lee & Jae-Jung Yun, 2019. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3576-:d:268562
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    References listed on IDEAS

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    1. 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.
    2. Harrag, Abdelghani & Messalti, Sabir, 2015. "Variable step size modified P&O MPPT algorithm using GA-based hybrid offline/online PID controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1247-1260.
    3. Liu, Yi-Hua & Chen, Jing-Hsiao & Huang, Jia-Wei, 2015. "A review of maximum power point tracking techniques for use in partially shaded conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 436-453.
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    Cited by:

    1. Miaomiao Ma & Xiangjie Liu & Kwang Y. Lee, 2020. "Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Abdelilah Chalh & Aboubakr El Hammoumi & Saad Motahhir & Abdelaziz El Ghzizal & Umashankar Subramaniam & Aziz Derouich, 2020. "Trusted Simulation Using Proteus Model for a PV System: Test Case of an Improved HC MPPT Algorithm," Energies, MDPI, vol. 13(8), pages 1-12, April.
    3. Ashwin Kumar Devarakonda & Natarajan Karuppiah & Tamilselvi Selvaraj & Praveen Kumar Balachandran & Ravivarman Shanmugasundaram & Tomonobu Senjyu, 2022. "A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems," Energies, MDPI, vol. 15(22), pages 1-30, November.
    4. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    5. Efrain Mendez-Flores & Alexandro Ortiz & Israel Macias & Arturo Molina, 2022. "Experimental Validation of an Enhanced MPPT Algorithm and an Optimal DC–DC Converter Design Powered by Metaheuristic Optimization for PV Systems," Energies, MDPI, vol. 15(21), pages 1-35, October.
    6. 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.
    7. Ehsan Jamshidpour & Slavisa Jovanovic & Philippe Poure, 2020. "Equivalent Two Switches and Single Switch Buck/Buck-Boost Circuits for Solar Energy Harvesting Systems," Energies, MDPI, vol. 13(3), pages 1-16, January.
    8. Efrain Mendez & Alexandro Ortiz & Pedro Ponce & Israel Macias & David Balderas & Arturo Molina, 2020. "Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm," Energies, MDPI, vol. 13(12), pages 1-24, June.
    9. Moacyr A. G. de Brito & Victor A. Prado & Edson A. Batista & Marcos G. Alves & Carlos A. Canesin, 2021. "Design Procedure to Convert a Maximum Power Point Tracking Algorithm into a Loop Control System," Energies, MDPI, vol. 14(15), pages 1-17, July.
    10. Marcin Walczak & Leszek Bychto, 2023. "Transients in Input and Output Signals in DC–DC Converters Working in Maximum Power Point Tracking Systems," Energies, MDPI, vol. 16(12), pages 1-12, June.
    11. Sajid Sarwar & Muhammad Yaqoob Javed & Mujtaba Hussain Jaffery & Muhammad Saqib Ashraf & Muhammad Talha Naveed & Muhammad Annas Hafeez, 2022. "Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions," Energies, MDPI, vol. 15(13), pages 1-39, June.

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