IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i10p1843-d231342.html
   My bibliography  Save this article

Flatness-Based Control for the Maximum Power Point Tracking in a Photovoltaic System

Author

Listed:
  • Leopoldo Gil-Antonio

    (Faculty of Engineering, Autonomous University of the State of Mexico, Instituto Literario No. 100 Oriente, Toluca 50130, Estado de México, Mexico
    Tecnológico de Estudios Superiores de Jocotitlán Carretera Toluca-Atlacomulco km. 44.8, Jocotitlán 50700, Estado de México, Mexico)

  • Belem Saldivar

    (Faculty of Engineering, Autonomous University of the State of Mexico, Instituto Literario No. 100 Oriente, Toluca 50130, Estado de México, Mexico
    Cátedras CONACYT, Av. Insurgentes Sur 1582, Col. Crédito Constructor, Alcaldía Benito Juárez, Ciudad de México 03940, Mexico)

  • Otniel Portillo-Rodríguez

    (Faculty of Engineering, Autonomous University of the State of Mexico, Instituto Literario No. 100 Oriente, Toluca 50130, Estado de México, Mexico)

  • Juan Carlos Ávila-Vilchis

    (Faculty of Engineering, Autonomous University of the State of Mexico, Instituto Literario No. 100 Oriente, Toluca 50130, Estado de México, Mexico)

  • Pánfilo Raymundo Martínez-Rodríguez

    (School of Sciencies, UASLP, San Luis Potosi 78290, SLP, Mexico)

  • Rigoberto Martínez-Méndez

    (Faculty of Engineering, Autonomous University of the State of Mexico, Instituto Literario No. 100 Oriente, Toluca 50130, Estado de México, Mexico)

Abstract

Solar energy harvesting using Photovoltaic (PV) systems is one of the most popular sources of renewable energy, however the main drawback of PV systems is their low conversion efficiency. An optimal system operation requires an efficient tracking of the Maximum Power Point (MPP), which represents the maximum energy that can be extracted from the PV panel. This paper presents a novel control approach for the Maximum Power Point Tracking (MPPT) based on the differential flatness property of the Boost converter, which is one of the most used converters in PV systems. The underlying idea of the proposed control approach is to use the classical flatness-based trajectory tracking control where a reference voltage will be defined in terms of the maximum power provided by the PV panel. The effectiveness of the proposed controller is assessed through numerical simulations and experimental tests. The results show that the controller based on differential flatness is capable of converging in less than 0.15 s and, compared with other MPPT techniques, such as Incremental Conductance and Perturb and Observe, it improves the response against sudden changes in load or weather conditions, reducing the ringing in the output of the system. Based on the results, it can be inferred that the new flatness-based controller represents an alternative to improve the MPPT in PV systems, especially when they are subject to sudden load or weather changes.

Suggested Citation

  • Leopoldo Gil-Antonio & Belem Saldivar & Otniel Portillo-Rodríguez & Juan Carlos Ávila-Vilchis & Pánfilo Raymundo Martínez-Rodríguez & Rigoberto Martínez-Méndez, 2019. "Flatness-Based Control for the Maximum Power Point Tracking in a Photovoltaic System," Energies, MDPI, vol. 12(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1843-:d:231342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/10/1843/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/10/1843/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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. Miaomiao Ma & Xiangjie Liu & Kwang Y. Lee, 2020. "Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    3. Víctor Hugo García-Rodríguez & José Humberto Pérez-Cruz & Roberto Carlos Ambrosio-Lázaro & Salvador Tavera-Mosqueda, 2023. "Analysis of DC/DC Boost Converter–Full-Bridge Buck Inverter System for AC Generation," Energies, MDPI, vol. 16(6), pages 1-16, March.
    4. Adeel Feroz Mirza & Majad Mansoor & Qiang Ling & Muhammad Imran Khan & Omar M. Aldossary, 2020. "Advanced Variable Step Size Incremental Conductance MPPT for a Standalone PV System Utilizing a GA-Tuned PID Controller," Energies, MDPI, vol. 13(16), pages 1-25, August.

    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. Moayed Almobaied & Hassan S. Al-Nahhal & Orlando Arrieta & Ramon Vilanova, 2023. "Design a Robust Proportional-Derivative Gain-Scheduling Control for a Magnetic Levitation System," Mathematics, MDPI, vol. 11(19), pages 1-21, September.
    2. Guerrero-Rodríguez, N.F. & Rey-Boué, Alexis B. & Herrero-de Lucas, Luis C. & Martinez-Rodrigo, Fernando, 2015. "Control and synchronization algorithms for a grid-connected photovoltaic system under harmonic distortions, frequency variations and unbalances," Renewable Energy, Elsevier, vol. 80(C), pages 380-395.
    3. Chettibi, N. & Mellit, A., 2018. "Intelligent control strategy for a grid connected PV/SOFC/BESS energy generation system," Energy, Elsevier, vol. 147(C), pages 239-262.
    4. Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    5. Kofinas, P. & Doltsinis, S. & Dounis, A.I. & Vouros, G.A., 2017. "A reinforcement learning approach for MPPT control method of photovoltaic sources," Renewable Energy, Elsevier, vol. 108(C), pages 461-473.
    6. Saud Alotaibi & Ahmed Darwish, 2021. "Modular Multilevel Converters for Large-Scale Grid-Connected Photovoltaic Systems: A Review," Energies, MDPI, vol. 14(19), pages 1-30, September.
    7. Wu, Zhenlong & Li, Donghai & Xue, Yali & Chen, YangQuan, 2019. "Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions," Energy, Elsevier, vol. 185(C), pages 744-762.
    8. Chun-Liang Liu & Jing-Hsiao Chen & Yi-Hua Liu & Zong-Zhen Yang, 2014. "An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 7(4), pages 1-17, April.
    9. Tabanjat, Abdulkader & Becherif, Mohamed & Hissel, Daniel, 2015. "Reconfiguration solution for shaded PV panels using switching control," Renewable Energy, Elsevier, vol. 82(C), pages 4-13.
    10. Hiyam Farhat & Coriolano Salvini, 2022. "Novel Gas Turbine Challenges to Support the Clean Energy Transition," Energies, MDPI, vol. 15(15), pages 1-17, July.
    11. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    12. Khaoula Ghefiri & Aitor J. Garrido & Eugen Rusu & Soufiene Bouallègue & Joseph Haggège & Izaskun Garrido, 2018. "Fuzzy Supervision Based-Pitch Angle Control of a Tidal Stream Generator for a Disturbed Tidal Input," Energies, MDPI, vol. 11(11), pages 1-21, November.
    13. Ali Bughneda & Mohamed Salem & Anna Richelli & Dahaman Ishak & Salah Alatai, 2021. "Review of Multilevel Inverters for PV Energy System Applications," Energies, MDPI, vol. 14(6), pages 1-23, March.
    14. 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.
    15. Ying Yang & Bin Wang & Yuqiang Tian & Peng Chen, 2020. "Fractional-Order Finite-Time, Fault-Tolerant Control of Nonlinear Hydraulic-Turbine-Governing Systems with an Actuator Fault," Energies, MDPI, vol. 13(15), pages 1-20, July.
    16. Yang, Libing & Entchev, Evgueniy & Ghorab, Mohamed & Lee, Euy-Joon & Kang, Eun-Chul & Kim, Yu-Jin & Nam, Yujin & Bae, Sangmu & Kim, Kwonye, 2022. "Advanced smart trigeneration energy system design for commercial building applications – Energy and cost performance analyses," Energy, Elsevier, vol. 259(C).
    17. 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.
    18. Khaoula Ghefiri & Izaskun Garrido & Soufiene Bouallègue & Joseph Haggège & Aitor J. Garrido, 2018. "Hybrid Neural Fuzzy Design-Based Rotational Speed Control of a Tidal Stream Generator Plant," Sustainability, MDPI, vol. 10(10), pages 1-26, October.
    19. Strušnik, Dušan & Avsec, Jurij, 2015. "Artificial neural networking and fuzzy logic exergy controlling model of combined heat and power system in thermal power plant," Energy, Elsevier, vol. 80(C), pages 318-330.
    20. 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.

    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:12:y:2019:i:10:p:1843-:d:231342. 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.