IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4798-d413430.html
   My bibliography  Save this article

Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels

Author

Listed:
  • Faiçal Hamidi

    (Laboratory “Modélisation, Analyse et Commande des Systèmes”, University of Gabes, LR16ES22 Gabes, Tunisia)

  • Severus Constantin Olteanu

    (Automatic Control and Systems Engineering Department, Automatic Control and Computer Science Faculty, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Dumitru Popescu

    (Automatic Control and Systems Engineering Department, Automatic Control and Computer Science Faculty, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Houssem Jerbi

    (Department of Industrial Engineering, College of Engineering, University of Ha’il, Hail 1234, Saudi Arabia)

  • Ingrid Dincă

    (Automatic Control and Systems Engineering Department, Automatic Control and Computer Science Faculty, University “Politehnica” of Bucharest, 060042 Bucharest, Romania)

  • Sondess Ben Aoun

    (Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Hail 1234, Saudi Arabia)

  • Rabeh Abbassi

    (Department of Electrical Engineering, College of Engineering, University of Ha’il, Hail 1234, Saudi Arabia)

Abstract

Extracting maximum energy from photovoltaic (PV) systems at varying conditions is crucial. It represents a problem that is being addressed by researchers who are using several techniques to obtain optimal outcomes in real-life scenarios. Among the many techniques, Maximum Power Point Tracking (MPPT) is one category that is not extensively researched upon. MPPT uses mathematical models to achieve gradient optimisation in the context of PV panels. This study proposes an enhanced maximisation problem based on gradient optimisation techniques to achieve better performance. In the context of MPPT in photovoltaic panels, an equality restriction applies, which is solved by employing the Dual Lagrangian expression. Considering this dual problem and its mathematical form, the Nesterov Accelerated Gradient (NAG) framework is used. Additionally, since it is challenging to ascertain the step size, its approximate value is taken using the Adadelta approach. A basic MPPT framework, along with a DC-to-DC convertor, was simulated to validate the results.

Suggested Citation

  • Faiçal Hamidi & Severus Constantin Olteanu & Dumitru Popescu & Houssem Jerbi & Ingrid Dincă & Sondess Ben Aoun & Rabeh Abbassi, 2020. "Model Based Optimisation Algorithm for Maximum Power Point Tracking in Photovoltaic Panels," Energies, MDPI, vol. 13(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4798-:d:413430
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4798/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4798/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    2. 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.
    3. Filip, Florin & Popescu, Dumitru & Mateescu, Mihaela, 2008. "Optimal decisions for complex systems—Software packages," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 76(5), pages 422-429.
    4. Khabou, H. & Souissi, M. & Aitouche, A., 2020. "MPPT implementation on boost converter by using T–S fuzzy method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 167(C), pages 119-134.
    5. Petru Adrian Cotfas & Daniel Tudor Cotfas & Paul Nicolae Borza & Dezso Sera & Remus Teodorescu, 2018. "Solar Cell Capacitance Determination Based on an RLC Resonant Circuit," Energies, MDPI, vol. 11(3), pages 1-13, March.
    6. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    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. Matej Bereš & Dobroslav Kováč & Tibor Vince & Irena Kováčová & Ján Molnár & Iveta Tomčíková & Jozef Dziak & Patrik Jacko & Branislav Fecko & Šimon Gans, 2021. "Efficiency Enhancement of Non-Isolated DC-DC Interleaved Buck Converter for Renewable Energy Sources," Energies, MDPI, vol. 14(14), pages 1-15, July.
    2. Wiktor Olchowik & Marcin Bednarek & Tadeusz Dąbrowski & Adam Rosiński, 2023. "Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems," Energies, MDPI, vol. 16(18), pages 1-24, September.
    3. Silvio Simani & Elena Zattoni, 2021. "Advanced Control Design and Fault Diagnosis," Energies, MDPI, vol. 14(18), pages 1-6, September.
    4. Waleed Al Abri & Rashid Al Abri & Hassan Yousef & Amer Al-Hinai, 2022. "A Maximum Power Point Tracker Using the Bald Eagle Search Technique for Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 15(23), pages 1-16, December.

    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. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    2. 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.
    3. 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).
    4. Hassan M. H. Farh & Mohd F. Othman & Ali M. Eltamaly & M. S. Al-Saud, 2018. "Maximum Power Extraction from a Partially Shaded PV System Using an Interleaved Boost Converter," Energies, MDPI, vol. 11(10), pages 1-18, September.
    5. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    6. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.
    7. Yin, Rumeng & He, Jiang, 2023. "Design of a photovoltaic electric bike battery-sharing system in public transit stations," Applied Energy, Elsevier, vol. 332(C).
    8. 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.
    9. Paula Andrea Ortiz Valencia & Carlos Andres Ramos-Paja, 2015. "Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 8(11), pages 1-25, November.
    10. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    11. Chatterjee, Shantanu & Kumar, Prashant & Chatterjee, Saibal, 2018. "A techno-commercial review on grid connected photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2371-2397.
    12. Tomáš Finsterle & Ladislava Černá & Pavel Hrzina & David Rokusek & Vítězslav Benda, 2021. "Diagnostics of PID Early Stage in PV Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
    13. Kamran Ali & Laiq Khan & Qudrat Khan & Shafaat Ullah & Saghir Ahmad & Sidra Mumtaz & Fazal Wahab Karam & Naghmash, 2019. "Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System," Energies, MDPI, vol. 12(16), pages 1-20, August.
    14. Yılmaz, Mehmet & Kaleli, Alirıza & Çorapsız, Muhammed Fatih, 2023. "Machine learning based dynamic super twisting sliding mode controller for increase speed and accuracy of MPPT using real-time data under PSCs," Renewable Energy, Elsevier, vol. 219(P1).
    15. Pillai, Dhanup S. & Rajasekar, N., 2018. "Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3503-3525.
    16. Ahmad, Riaz & Murtaza, Ali F. & Sher, Hadeed Ahmed, 2019. "Power tracking techniques for efficient operation of photovoltaic array in solar applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 82-102.
    17. 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.
    18. Andrés Tobón & Julián Peláez-Restrepo & Jhon Montano & Mariana Durango & Jorge Herrera & Asier Ibeas, 2020. "MPPT of a Photovoltaic Panels Array with Partial Shading Using the IPSM with Implementation Both in Simulation as in Hardware," Energies, MDPI, vol. 13(4), pages 1-17, February.
    19. Fahd A. Alturki & Abdullrahman A. Al-Shamma’a & Hassan M. H. Farh, 2020. "Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    20. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.

    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:gam:jeners:v:13:y:2020:i:18:p:4798-:d:413430. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.