IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v74y2017icp1147-1169.html
   My bibliography  Save this article

A new MPPT scheme based on a novel fuzzy approach

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032117302757
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2017.02.054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Eltawil, Mohamed A. & Zhao, Zhengming, 2013. "MPPT techniques for photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 793-813.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
    2. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    3. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    4. 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.
    5. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    6. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
    7. 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.
    8. 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.
    9. Ram, J. Prasanth & Babu, T. Sudhakar & Rajasekar, N., 2017. "A comprehensive review on solar PV maximum power point tracking techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 826-847.
    10. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    11. Andrés Tobón & Julián Peláez-Restrepo & Juan P. Villegas-Ceballos & Sergio Ignacio Serna-Garcés & Jorge Herrera & Asier Ibeas, 2017. "Maximum Power Point Tracking of Photovoltaic Panels by Using Improved Pattern Search Methods," Energies, MDPI, vol. 10(9), pages 1-15, September.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Joshi, Puneet & Arora, Sudha, 2017. "Maximum power point tracking methodologies for solar PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1154-1177.
    17. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.
    18. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    19. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.
    20. Zheng, Shiyong & Shahzad, Muhammad & Asif, Hafiz Muhammad & Gao, Jing & Muqeet, Hafiz Abdul, 2023. "Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies," Renewable Energy, Elsevier, vol. 206(C), pages 1326-1335.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:74:y:2017:i:c:p:1147-1169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.