IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v35y2010i10p2381-2387.html
   My bibliography  Save this article

Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms

Author

Listed:
  • Houssamo, Issam
  • Locment, Fabrice
  • Sechilariu, Manuela

Abstract

This work presents an experimental comparison of two algorithms developed in order to maximize the output power from a photovoltaic (PV) system for the same given set of conditions. The maximum power point tracking (MPPT) methods proposed in this study are two extended algorithms: Perturb and Observe and Incremental Conductance. The numerical modelling of the PV system shows the MPPT interest and then the extended MPPT algorithms are highlighted. In this paper, a PV system based on a boost converter as MPPT device is considered. A programmable DC electronic load is fed by two identical PV systems in which the MPPT control converter algorithms are different. This experimental platform operates under the same conditions such as changing solar radiation and cell temperature. The experimental results obtained with a dSPACE controller board show the MPPT energy efficiency of the proposed algorithms.

Suggested Citation

  • Houssamo, Issam & Locment, Fabrice & Sechilariu, Manuela, 2010. "Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms," Renewable Energy, Elsevier, vol. 35(10), pages 2381-2387.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:10:p:2381-2387
    DOI: 10.1016/j.renene.2010.04.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148110001588
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2010.04.006?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. Kang, Feel-soon & Park, Sung-Jun & Cho, Su Eog & Kim, Jang-Mok, 2005. "Photovoltaic power interface circuit incorporated with a buck-boost converter and a full-bridge inverter," Applied Energy, Elsevier, vol. 82(3), pages 266-283, November.
    2. Tafticht, T. & Agbossou, K. & Doumbia, M.L. & Chériti, A., 2008. "An improved maximum power point tracking method for photovoltaic systems," Renewable Energy, Elsevier, vol. 33(7), pages 1508-1516.
    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. Kim, Ho-sung & Kim, Jong-Hyun & Min, Byung-Duk & Yoo, Dong-Wook & Kim, Hee-Je, 2009. "A highly efficient PV system using a series connection of DC–DC converter output with a photovoltaic panel," Renewable Energy, Elsevier, vol. 34(11), pages 2432-2436.
    5. Hamrouni, N. & Jraidi, M. & Chérif, A., 2008. "New control strategy for 2-stage grid-connected photovoltaic power system," Renewable Energy, Elsevier, vol. 33(10), pages 2212-2221.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Amir, Asim & Amir, Aamir & Che, Hang Seng & Elkhateb, Ahmad & Rahim, Nasrudin Abd, 2019. "Comparative analysis of high voltage gain DC-DC converter topologies for photovoltaic systems," Renewable Energy, Elsevier, vol. 136(C), pages 1147-1163.
    3. Nasiri, Reza & Radan, Ahmad, 2011. "Adaptive decoupled control of 4-leg voltage-source inverters for standalone photovoltaic systems: Adjusting transient state response," Renewable Energy, Elsevier, vol. 36(10), pages 2733-2741.
    4. 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.
    5. Choi, Woo-Young & Lee, Change-Goo, 2012. "Photovoltaic panel integrated power conditioning system using a high efficiency step-up DC–DC converter," Renewable Energy, Elsevier, vol. 41(C), pages 227-234.
    6. 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.
    7. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    8. Al-Saffar, Mustafa A. & Ismail, Esam H. & Sabzali, Ahmad J., 2013. "Family of ZC-ZVS converters with wide voltage range for renewable energy systems," Renewable Energy, Elsevier, vol. 56(C), pages 32-43.
    9. Nasiri, Reza & Radan, Ahmad, 2011. "Pole-placement control of 4-leg voltage-source inverters for standalone photovoltaic systems: Considering digital delays," Renewable Energy, Elsevier, vol. 36(2), pages 858-865.
    10. Nasiri, Reza & Radan, Ahmad, 2011. "Adaptive pole-placement control of 4-leg voltage-source inverters for standalone photovoltaic systems," Renewable Energy, Elsevier, vol. 36(7), pages 2032-2042.
    11. 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.
    12. Datta, Manoj & Senjyu, Tomonobu & Yona, Atsushi & Funabashi, Toshihisa, 2011. "A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system," Renewable Energy, Elsevier, vol. 36(6), pages 1693-1703.
    13. 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.
    14. Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
    15. Peng Zhang & Huibin Sui, 2020. "Maximum Power Point Tracking Technology of Photovoltaic Array under Partial Shading Based On Adaptive Improved Differential Evolution Algorithm," Energies, MDPI, vol. 13(5), pages 1-15, March.
    16. Sivakumar, S. & Sathik, M. Jagabar & Manoj, P.S. & Sundararajan, G., 2016. "An assessment on performance of DC–DC converters for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1475-1485.
    17. 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.
    18. Yi Jin & Wenhui Hou & Guiqiang Li & Xiao Chen, 2017. "A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution," Energies, MDPI, vol. 10(4), pages 1-13, April.
    19. Irfan, Muhammad & Iqbal, Jamshed & Iqbal, Adeel & Iqbal, Zahid & Riaz, Raja Ali & Mehmood, Adeel, 2017. "Opportunities and challenges in control of smart grids – Pakistani perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 652-674.
    20. Chih-Hong Lin, 2016. "Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate," Energies, MDPI, vol. 9(6), pages 1-25, June.

    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:renene:v:35:y:2010:i:10:p:2381-2387. 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/renewable-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.