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A Review and New Problems Discovery of Four Simple Decentralized Maximum Power Point Tracking Algorithms—Perturb and Observe, Incremental Conductance, Golden Section Search, and Newton’s Quadratic Interpolation

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  • Victor Andrean

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, 2F, EE, No. 43, Sec. 4, Keelung Rd, Taipei 106, Taiwan
    Current address: Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan.
    These authors contributed equally to this work.)

  • Pei Cheng Chang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, 2F, EE, No. 43, Sec. 4, Keelung Rd, Taipei 106, Taiwan
    These authors contributed equally to this work.)

  • Kuo Lung Lian

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, 2F, EE, No. 43, Sec. 4, Keelung Rd, Taipei 106, Taiwan
    Current address: Electrical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan.)

Abstract

Maximum Power Point Tracking (MPPT) enables photovoltaic (PV) systems to extract as much solar energy as possible. Depending on which type of controller is used, PV systems can be classified as centralized MPPT (CMPPT) or decentralized MPPT (DMPPT). In substring-level systems, it is known that the energy yield of DMPPT can outweigh the power electronics cost. At the substring level, it is usually assumed that the PV curve exhibits a single peak, even under partial shading. Thus, the control algorithms for DMPPT are usually less complicated than those employed in CMPPT systems. This paper provides a comprehensive review of four simple DMPPT algorithms, which are perturb and observe (P&O), incremental conductance (INC), golden section search (GSS), and Newton’s quadratic interpolation (NQI). The comparison of these algorithms are done from the perspective of numerical analysis. Guidelines on how to set initial conditions and convergence criteria are thoroughly explained. This is of great interest to PV engineers when selecting algorithms for use in MPPT implementations. In addition, various problems that have never previously been identified before are highlighted and discussed. For instance, the problems of NQI trap is identified and methods on how to mitigate it are also discussed. All the algorithms are tested under various conditions including static, dynamic, and rapid changes of irradiance. Both simulation and experimental results indicate that P&O and INC are the best algorithms for DMPPT.

Suggested Citation

  • Victor Andrean & Pei Cheng Chang & Kuo Lung Lian, 2018. "A Review and New Problems Discovery of Four Simple Decentralized Maximum Power Point Tracking Algorithms—Perturb and Observe, Incremental Conductance, Golden Section Search, and Newton’s Quadratic Int," Energies, MDPI, vol. 11(11), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2966-:d:179651
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    References listed on IDEAS

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    1. Amir, A. & Amir, A. & Selvaraj, J. & Rahim, N.A., 2016. "Study of the MPP tracking algorithms: Focusing the numerical method techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 350-371.
    2. 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.
    3. Danandeh, M.A. & Mousavi G., S.M., 2018. "Comparative and comprehensive review of maximum power point tracking methods for PV cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2743-2767.
    4. Eltawil, Mohamed A. & Zhao, Zhengming, 2013. "MPPT techniques for photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 793-813.
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    2. Eyal Amer & Alon Kuperman & Teuvo Suntio, 2019. "Direct Fixed-Step Maximum Power Point Tracking Algorithms with Adaptive Perturbation Frequency," Energies, MDPI, vol. 12(3), pages 1-16, January.
    3. Ming-Fa Tsai & Chung-Shi Tseng & Kuo-Tung Hung & Shih-Hua Lin, 2021. "A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator," Energies, MDPI, vol. 14(11), pages 1-20, June.
    4. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    5. Amjad Ali & Kashif Irshad & Mohammad Farhan Khan & Md Moinul Hossain & Ibrahim N. A. Al-Duais & Muhammad Zeeshan Malik, 2021. "Artificial Intelligence and Bio-Inspired Soft Computing-Based Maximum Power Plant Tracking for a Solar Photovoltaic System under Non-Uniform Solar Irradiance Shading Conditions—A Review," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    6. 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.
    7. 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.
    8. Nahla E. Zakzouk & Ahmed K. Khamis & Ahmed K. Abdelsalam & Barry W. Williams, 2019. "Continuous-Input Continuous-Output Current Buck-Boost DC/DC Converters for Renewable Energy Applications: Modelling and Performance Assessment," Energies, MDPI, vol. 12(11), pages 1-27, June.
    9. Amjad Ali & K. Almutairi & Muhammad Zeeshan Malik & Kashif Irshad & Vineet Tirth & Salem Algarni & Md. Hasan Zahir & Saiful Islam & Md Shafiullah & Neeraj Kumar Shukla, 2020. "Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions," Energies, MDPI, vol. 13(12), pages 1-37, June.
    10. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.

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