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Design and Experimental Implementation of a Hysteresis Algorithm to Optimize the Maximum Power Point Extracted from a Photovoltaic System

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  • Nubia Ilia Ponce de León Puig

    () (Department of Mathematics, Escola d’Enginyeria de Barcelona Est-EEBE, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), 08034 Barcelona, Spain)

  • Leonardo Acho

    () (Department of Mathematics, Escola d’Enginyeria de Barcelona Est-EEBE, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), 08034 Barcelona, Spain)

  • José Rodellar

    () (Department of Mathematics, Escola d’Enginyeria de Barcelona Est-EEBE, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), 08034 Barcelona, Spain)

Abstract

In the several last years, numerous Maximum Power Point Tracking (MPPT) methods for photovoltaic (PV) systems have been proposed. An MPPT strategy is necessary to ensure the maximum power efficiency provided to the load from a PV module that is subject to external environmental perturbations such as radiance, temperature and partial shading. In this paper, a new MPPT technique is presented. Our approach has the novelty that it is a MPPT algorithm with a dynamic hysteresis model incorporated. One of the most cited Maximum Power Point Tracking methods is the Perturb and Observer algorithm since it is easily implemented. A comparison between the approach presented in this paper and the known Perturb and Observer method is evaluated. Moreover, a new PV-system platform was properly designed by employing low cost electronics, which may serve as an academical platform for further research and developments. This platform is used to show that the proposed algorithm is more efficient than the standard Perturb and Observer method.

Suggested Citation

  • 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, Open Access Journal, vol. 11(7), pages 1-24, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1866-:d:158414
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    References listed on IDEAS

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    1. Tahsin Fahima Orchi & Md Apel Mahmud & Amanullah Maung Than Oo, 2018. "Generalized Dynamical Modeling of Multiple Photovoltaic Units in a Grid-Connected System for Analyzing Dynamic Interactions," Energies, MDPI, Open Access Journal, vol. 11(2), pages 1-12, January.
    2. Jinpeng Liu & Yun Long & Xiaohua Song, 2017. "A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China," Energies, MDPI, Open Access Journal, vol. 10(5), pages 1-22, May.
    3. Dae-Jin Kim & Byungki Kim & Hee-Sang Ko & Moon-Seok Jang & Kyung-Sang Ryu, 2017. "A Novel Supervisory Control Algorithm to Improve the Performance of a Real-Time PV Power-Hardware-In-Loop Simulator with Non-RTDS," Energies, MDPI, Open Access Journal, vol. 10(10), pages 1-16, October.
    4. 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.
    5. 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.
    6. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "Economic Analysis of a Photovoltaic System: A Resource for Residential Households," Energies, MDPI, Open Access Journal, vol. 10(6), pages 1-15, June.
    7. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    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. Manel Hammami & Gabriele Grandi, 2017. "A Single-Phase Multilevel PV Generation System with an Improved Ripple Correlation Control MPPT Algorithm," Energies, MDPI, Open Access Journal, vol. 10(12), pages 1-19, December.
    10. Vieira, Filomeno M. & Moura, Pedro S. & de Almeida, Aníbal T., 2017. "Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings," Renewable Energy, Elsevier, vol. 103(C), pages 308-320.
    11. Rodrigues, E.M.G. & Osório, G.J. & Godina, R. & Bizuayehu, A.W. & Lujano-Rojas, J.M. & Catalão, J.P.S., 2016. "Grid code reinforcements for deeper renewable generation in insular energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 163-177.
    12. Farhat, Maissa & Barambones, Oscar & Sbita, Lassaad, 2017. "A new maximum power point method based on a sliding mode approach for solar energy harvesting," Applied Energy, Elsevier, vol. 185(P2), pages 1185-1198.
    13. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, Open Access Journal, vol. 10(12), pages 1-18, December.
    14. Cabrera-Tobar, Ana & Bullich-Massagué, Eduard & Aragüés-Peñalba, Mònica & Gomis-Bellmunt, Oriol, 2016. "Review of advanced grid requirements for the integration of large scale photovoltaic power plants in the transmission system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 971-987.
    15. Qiang Ni & Shengxian Zhuang & Hanmin Sheng & Song Wang & Jian Xiao, 2017. "An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting," Energies, MDPI, Open Access Journal, vol. 10(10), pages 1-16, October.
    16. Enany, Mohamed A. & Farahat, Mohamed A. & Nasr, Ahmed, 2016. "Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1578-1586.
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    More about this item

    Keywords

    maximum power point tracking; photovoltaic system; hysteresis; modeling; electronic design;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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