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A new MPPT scheme based on a novel fuzzy approach

Author

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  • Nabipour, M.
  • Razaz, M.
  • Seifossadat, S.GH
  • Mortazavi, S.S.

Abstract

In this paper, after defining Maximum Power Point Tracking (MPPT) control expectations, with the aim of finding the optimum routine in fulfilling these expectations, a review over the available MPPT control methods is presented. Throughout the review, by comparing conventional MPPT routines in terms of accomplishing defined control objectives, the necessity of designing a new MPPT control scheme based on adaptive fuzzy logic is expressed. Based on the conducted review, a new routine to optimize the MPPT performance of a Photovoltaic (PV)-setup and to fulfill all the MPPT control requirements is proposed. The optimization is performed in tracking the Maximum Power Point (MPP) of the PV-module by a Boost-converter using an “antecedent-consequent adaptive” indirect fuzzy-based MPPT scheme. The fuzzy-based scheme is tuned online using a novel computationally light membership function tuning routine, where the antecedent and consequent membership functions are tuned synchronously. As a result, a fast, smooth and computationally light MPPT controller is proposed. In this regard, the presented MPPT scheme tuned using the proposed novel tuning routine is compared with conventional direct and indirect fuzzy-based MPPT schemes, showing superiority of the proposed MPPT routine over conventional schemes.

Suggested Citation

  • Nabipour, M. & Razaz, M. & Seifossadat, S.GH & Mortazavi, S.S., 2017. "A new MPPT scheme based on a novel fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1147-1169.
    • Handle: RePEc:eee:rensus:v:74:y:2017:i:c:p:1147-1169
    DOI: 10.1016/j.rser.2017.02.054
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    1. Daraban, Stefan & Petreus, Dorin & Morel, Cristina, 2014. "A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading," Energy, Elsevier, vol. 74(C), pages 374-388.
    2. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2014. "Enhancing the maximum power point tracking techniques for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 505-514.
    3. Larbes, C. & Aït Cheikh, S.M. & Obeidi, T. & Zerguerras, A., 2009. "Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system," Renewable Energy, Elsevier, vol. 34(10), pages 2093-2100.
    4. Wei, Haokun & Liu, Jian & Yang, Biao, 2014. "Cost-benefit comparison between Domestic Solar Water Heater (DSHW) and Building Integrated Photovoltaic (BIPV) systems for households in urban China," Applied Energy, Elsevier, vol. 126(C), pages 47-55.
    5. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
    6. Mikati, M. & Santos, M. & Armenta, C., 2013. "Electric grid dependence on the configuration of a small-scale wind and solar power hybrid system," Renewable Energy, Elsevier, vol. 57(C), pages 587-593.
    7. Hafiz, Faizal & Abdennour, Adel, 2016. "An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines," Renewable Energy, Elsevier, vol. 92(C), pages 136-146.
    8. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    9. Zhang, Wei & Zhu, Rui & Liu, Bin & Ramakrishna, Seeram, 2012. "High-performance hybrid solar cells employing metal-free organic dye modified TiO2 as photoelectrode," Applied Energy, Elsevier, vol. 90(1), pages 305-308.
    10. Reza Reisi, Ali & Hassan Moradi, Mohammad & Jamasb, Shahriar, 2013. "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 433-443.
    11. Bahgat, A.B.G. & Helwa, N.H. & Ahmad, G.E. & El Shenawy, E.T., 2005. "Maximum power point traking controller for PV systems using neural networks," Renewable Energy, Elsevier, vol. 30(8), pages 1257-1268.
    12. 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.
    13. 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.
    14. Eltawil, Mohamed A. & Zhao, Zhengming, 2013. "MPPT techniques for photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 793-813.
    15. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    16. Messalti, Sabir & Harrag, Abdelghani & Loukriz, Abdelhamid, 2017. "A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 221-233.
    17. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaâd, 2015. "Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 907-920.
    18. Po-Chen Cheng & Bo-Rei Peng & Yi-Hua Liu & Yu-Shan Cheng & Jia-Wei Huang, 2015. "Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique," Energies, MDPI, vol. 8(6), pages 1-23, June.
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    1. Wang, Jian-jun & Deng, Yu-cong & Sun, Wen-biao & Zheng, Xiao-bin & Cui, Zheng, 2023. "Maximum power point tracking method based on impedance matching for a micro hydropower generator," Applied Energy, Elsevier, vol. 340(C).

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