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Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems

Author

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  • Boukenoui, R.
  • Ghanes, M.
  • Barbot, J.-P.
  • Bradai, R.
  • Mellit, A.
  • Salhi, H.

Abstract

This paper presents different Maximum Power Point Tracking (MPPT) methods belonging to different classes as well as two overviews. The first was about the procedures used in the test and evaluation of MPPTs. The second is an overview of Fuzzy Logic Controller (FLC) MPPTs and improved MPPTs. Conventional MPPTs such as Perturb and Observe (P&O), Hill Climbing (HC) and Incremental Conductance (InCond); Improved MPPTs (are the modified versions of conventional MPPTs) such as Improved Incremental Conductance (Improved-InCond) and intelligent MPPTs such as FLC have been implemented and tested under two different levels of irradiance and temperature. A detailed description about the hardware and software implementation platforms (designed and built in our laboratory) is provided. Based on measured data, the MPPTs under consideration have been evaluated and compared in terms of different criteria, showing the advantages and disadvantages of each one. The comparison results showed that Improved-InCond gives a fast convergence to the MPP(Maximum Power Point). Whereas, FLC is able to adapt to the variation of irradiance and temperature levels. Thereby, a good performance is obtained wherein the MPP is reached in a short time as well as the power ripples are very small.

Suggested Citation

  • Boukenoui, R. & Ghanes, M. & Barbot, J.-P. & Bradai, R. & Mellit, A. & Salhi, H., 2017. "Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems," Energy, Elsevier, vol. 132(C), pages 324-340.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:324-340
    DOI: 10.1016/j.energy.2017.05.087
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    Cited by:

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    2. Kishore, D.J. Krishna & Mohamed, M.R. & Sudhakar, K. & Peddakapu, K., 2023. "Swarm intelligence-based MPPT design for PV systems under diverse partial shading conditions," Energy, Elsevier, vol. 265(C).
    3. Aatabe, Mohamed & El Guezar, Fatima & Vargas, Alessandro N. & Bouzahir, Hassane, 2021. "A novel stochastic maximum power point tracking control for off-grid standalone photovoltaic systems with unpredictable load demand," Energy, Elsevier, vol. 235(C).
    4. Makhsoos, Ashkan & Mousazadeh, Hossein & Mohtasebi, Seyed Saeid & Abdollahzadeh, Mohammadreza & Jafarbiglu, Hamid & Omrani, Elham & Salmani, Yousef & Kiapey, Ali, 2018. "Design, simulation and experimental evaluation of energy system for an unmanned surface vehicle," Energy, Elsevier, vol. 148(C), pages 362-372.
    5. Blaifi, Sid-ali & Moulahoum, Samir & Taghezouit, Bilal & Saim, Abdelhakim, 2019. "An enhanced dynamic modeling of PV module using Levenberg-Marquardt algorithm," Renewable Energy, Elsevier, vol. 135(C), pages 745-760.
    6. Nubia Ilia Ponce de León Puig & Leonardo Acho & José Rodellar, 2018. "Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System," Energies, MDPI, vol. 11(7), pages 1-24, July.
    7. Mao, Mingxuan & Zhang, Li & Duan, Pan & Duan, Qichang & Yang, Ming, 2018. "Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller," Energy, Elsevier, vol. 143(C), pages 181-190.

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