IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v132y2017icp324-340.html
   My bibliography  Save this article

Experimental assessment of Maximum Power Point Tracking methods for photovoltaic systems

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217308484
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.05.087?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. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    3. Mohanty, Parimita & Bhuvaneswari, G. & Balasubramanian, R. & Dhaliwal, Navdeep Kaur, 2014. "MATLAB based modeling to study the performance of different MPPT techniques used for solar PV system under various operating conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 581-593.
    4. Ishaque, Kashif & Salam, Zainal & Lauss, George, 2014. "The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions," Applied Energy, Elsevier, vol. 119(C), pages 228-236.
    5. Weiss, Olga & Bogdanov, Dmitry & Salovaara, Kaisa & Honkapuro, Samuli, 2017. "Market designs for a 100% renewable energy system: Case isolated power system of Israel," Energy, Elsevier, vol. 119(C), pages 266-277.
    6. 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.
    7. Bana, Sangram & Saini, R.P., 2017. "Experimental investigation on power output of different photovoltaic array configurations under uniform and partial shading scenarios," Energy, Elsevier, vol. 127(C), pages 438-453.
    8. 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.
    9. Rajesh, R. & Mabel, M. Carolin, 2016. "Design and real time implementation of a novel rule compressed fuzzy logic method for the determination operating point in a photo voltaic system," Energy, Elsevier, vol. 116(P1), pages 140-153.
    10. Solomon, A.A. & Kammen, Daniel M. & Callaway, D., 2016. "Investigating the impact of wind–solar complementarities on energy storage requirement and the corresponding supply reliability criteria," Applied Energy, Elsevier, vol. 168(C), pages 130-145.
    11. Hong, Chih-Ming & Ou, Ting-Chia & Lu, Kai-Hung, 2013. "Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system," Energy, Elsevier, vol. 50(C), pages 270-279.
    12. Belkaid, A. & Colak, I. & Isik, O., 2016. "Photovoltaic maximum power point tracking under fast varying of solar radiation," Applied Energy, Elsevier, vol. 179(C), pages 523-530.
    13. 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.
    14. Verma, Deepak & Nema, Savita & Shandilya, A.M. & Dash, Soubhagya K., 2016. "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1018-1034.
    15. Bouilouta, A. & Mellit, A. & Kalogirou, S.A., 2013. "New MPPT method for stand-alone photovoltaic systems operating under partially shaded conditions," Energy, Elsevier, vol. 55(C), pages 1172-1185.
    16. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    17. 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.
    18. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    19. Ahmed, Jubaer & Salam, Zainal, 2015. "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, Elsevier, vol. 150(C), pages 97-108.
    20. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
    21. Mamarelis, Emilio & Petrone, Giovanni & Spagnuolo, Giovanni, 2014. "A two-steps algorithm improving the P&O steady state MPPT efficiency," Applied Energy, Elsevier, vol. 113(C), pages 414-421.
    22. Dounis, Anastasios I. & Kofinas, Panagiotis & Alafodimos, Constantine & Tseles, Dimitrios, 2013. "Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system," Renewable Energy, Elsevier, vol. 60(C), pages 202-214.
    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. Obeidi, Nabil & Kermadi, Mostefa & Belmadani, Bachir & Allag, Abdelkrim & Achour, Lazhar & Mesbahi, Nadhir & Mekhilef, Saad, 2023. "A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems," Energy, Elsevier, vol. 263(PA).
    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.

    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. 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.
    2. 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.
    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. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    5. 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.
    6. Fathabadi, Hassan, 2016. "Novel fast dynamic MPPT (maximum power point tracking) technique with the capability of very high accurate power tracking," Energy, Elsevier, vol. 94(C), pages 466-475.
    7. Fathabadi, Hassan, 2016. "Novel high accurate sensorless dual-axis solar tracking system controlled by maximum power point tracking unit of photovoltaic systems," Applied Energy, Elsevier, vol. 173(C), pages 448-459.
    8. Sheik Mohammed, S. & Devaraj, D. & Imthias Ahamed, T.P., 2016. "A novel hybrid Maximum Power Point Tracking Technique using Perturb & Observe algorithm and Learning Automata for solar PV system," Energy, Elsevier, vol. 112(C), pages 1096-1106.
    9. Li, Qiyu & Zhao, Shengdun & Wang, Mengqi & Zou, Zhongyue & Wang, Bin & Chen, Qixu, 2017. "An improved perturbation and observation maximum power point tracking algorithm based on a PV module four-parameter model for higher efficiency," Applied Energy, Elsevier, vol. 195(C), pages 523-537.
    10. Fathabadi, Hassan, 2017. "Novel grid-connected solar/wind powered electric vehicle charging station with vehicle-to-grid technology," Energy, Elsevier, vol. 132(C), pages 1-11.
    11. Yinxiao Zhu & Moon Keun Kim & Huiqing Wen, 2018. "Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics," Energies, MDPI, vol. 12(1), pages 1-20, December.
    12. 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.
    13. Bradai, R. & Boukenoui, R. & Kheldoun, A. & Salhi, H. & Ghanes, M. & Barbot, J-P. & Mellit, A., 2017. "Experimental assessment of new fast MPPT algorithm for PV systems under non-uniform irradiance conditions," Applied Energy, Elsevier, vol. 199(C), pages 416-429.
    14. 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.
    15. Fathabadi, Hassan, 2017. "Novel standalone hybrid solar/wind/fuel cell/battery power generation system," Energy, Elsevier, vol. 140(P1), pages 454-465.
    16. Başoğlu, Mustafa Engin & Çakır, Bekir, 2016. "Comparisons of MPPT performances of isolated and non-isolated DC–DC converters by using a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1100-1113.
    17. 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.
    18. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    19. Ahmad M. A. Malkawi & Abdallah Odat & Ahmad Bashaireh, 2022. "A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    20. 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.

    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:energy:v:132:y:2017:i:c:p:324-340. 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.journals.elsevier.com/energy .

    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.