IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i21p8043-d957057.html
   My bibliography  Save this article

Experimental Validation of an Enhanced MPPT Algorithm and an Optimal DC–DC Converter Design Powered by Metaheuristic Optimization for PV Systems

Author

Listed:
  • Efrain Mendez-Flores

    (Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92617, USA
    Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Alexandro Ortiz

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Israel Macias

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

  • Arturo Molina

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico
    These authors contributed equally to this work.)

Abstract

Nowadays, photovoltaic (PV) systems are responsible for over 994 TWH of the worldwide energy supply, which highlights their relevance and also explains why so much research has arisen to enhance their implementation; among this research, different optimization techniques have been widely studied to maximize the energy harvested under different environmental conditions (maximum power point tracking) and to optimize the efficiency of the required power electronics for the implementation of MPPT algorithms. On the one hand, an earthquake optimization algorithm (EA) was introduced as a multi-objective optimization tool for DC–DC converter design, mostly to overcome component shortages by optimal replacement, but it had never been tested (until now) for PV applications. On the other hand, the original EA was also taken as inspiration for a promising EA-based MPPT, which presumably enabled a solution with simple parametric calibration and improved dynamic behavior; yet prior to this research, the EA-MPPT had never been experimentally validated. Hence, this work fills the gap and provides the first implementation of the EA-based MPPT, validating its performance and suitability under real physical conditions, where the experimental testbed was optimized through the EA design methodology for DC–DC converters and implemented for the first time for PV applications. The results present energy waste reduction between 12 and 36% compared to MPPTs based on perturb and observe and particle swarm optimization; meanwhile, the designed converter achieved 7.3% current ripple, which is between 2.7 and 12.7% less than some industrial converters, and it had almost 90% efficiency at nominal operation. Finally, the EA-MPPT proved simple enough to be implemented even through an 8-bit MCU (ATmega328P from Arduino UNO).

Suggested Citation

  • Efrain Mendez-Flores & Alexandro Ortiz & Israel Macias & Arturo Molina, 2022. "Experimental Validation of an Enhanced MPPT Algorithm and an Optimal DC–DC Converter Design Powered by Metaheuristic Optimization for PV Systems," Energies, MDPI, vol. 15(21), pages 1-35, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8043-:d:957057
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/21/8043/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/21/8043/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyeon-Seok Lee & Jae-Jung Yun, 2019. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-17, September.
    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. 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.
    4. Efrain Mendez & Alexandro Ortiz & Pedro Ponce & Israel Macias & David Balderas & Arturo Molina, 2020. "Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm," Energies, MDPI, vol. 13(12), pages 1-24, June.
    5. Jayalakshmi N. Sabhahit & Sanjana Satish Solanke & Vinay Kumar Jadoun & Hasmat Malik & Fausto Pedro García Márquez & Jesús María Pinar-Pérez, 2022. "Contingency Analysis of a Grid Connected EV's for Primary Frequency Control of an Industrial Microgrid Using Efficient Control Scheme," Energies, MDPI, vol. 15(9), pages 1-24, April.
    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. Yazhou Zhao & Xiangxi Qin & Xiangyu Shi, 2022. "A Comprehensive Evaluation Model on Optimal Operational Schedules for Battery Energy Storage System by Maximizing Self-Consumption Strategy and Genetic Algorithm," Sustainability, MDPI, vol. 14(14), pages 1-34, July.
    2. Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.
    3. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.
    4. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    5. 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.
    6. Mirza, Adeel Feroz & Mansoor, Majad & Zhan, Keyu & Ling, Qiang, 2021. "High-efficiency swarm intelligent maximum power point tracking control techniques for varying temperature and irradiance," Energy, Elsevier, vol. 228(C).
    7. Marcin Walczak & Leszek Bychto, 2023. "Transients in Input and Output Signals in DC–DC Converters Working in Maximum Power Point Tracking Systems," Energies, MDPI, vol. 16(12), pages 1-12, June.
    8. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    9. Alexandro Ortiz & Efrain Mendez & Israel Macias & Arturo Molina, 2022. "Earthquake Algorithm-Based Voltage Referenced MPPT Implementation through a Standardized Validation Frame," Energies, MDPI, vol. 15(23), pages 1-24, November.
    10. Eneko Artetxe & Jokin Uralde & Oscar Barambones & Isidro Calvo & Imanol Martin, 2023. "Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    11. Víctor Ferreira Gruner & Jefferson William Zanotti & Walbermark Marques Santos & Thiago Antonio Pereira & Lenon Schmitz & Denizar Cruz Martins & Roberto Francisco Coelho, 2023. "Modified Current Sensorless Incremental Conductance Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 16(2), pages 1-16, January.
    12. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    13. Efrain Mendez & Alexandro Ortiz & Pedro Ponce & Israel Macias & David Balderas & Arturo Molina, 2020. "Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm," Energies, MDPI, vol. 13(12), pages 1-24, June.
    14. 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.
    15. Moacyr A. G. de Brito & Victor A. Prado & Edson A. Batista & Marcos G. Alves & Carlos A. Canesin, 2021. "Design Procedure to Convert a Maximum Power Point Tracking Algorithm into a Loop Control System," Energies, MDPI, vol. 14(15), pages 1-17, July.
    16. Sajid Sarwar & Muhammad Yaqoob Javed & Mujtaba Hussain Jaffery & Muhammad Saqib Ashraf & Muhammad Talha Naveed & Muhammad Annas Hafeez, 2022. "Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions," Energies, MDPI, vol. 15(13), pages 1-39, June.
    17. 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.
    18. Bassam A. Hemad & Nader M. A. Ibrahim & Shereen A. Fayad & Hossam E. A. Talaat, 2022. "Hierarchical Clustering-Based Framework for Interconnected Power System Contingency Analysis," Energies, MDPI, vol. 15(15), pages 1-12, August.
    19. Ehsan Jamshidpour & Slavisa Jovanovic & Philippe Poure, 2020. "Equivalent Two Switches and Single Switch Buck/Buck-Boost Circuits for Solar Energy Harvesting Systems," Energies, MDPI, vol. 13(3), pages 1-16, January.
    20. Domenico Curto & Vincenzo Franzitta & Andrea Guercio & Rosario Miceli & Claudio Nevoloso & Francesco Maria Raimondi & Marco Trapanese, 2022. "An Experimental Comparison between an Ironless and a Traditional Permanent Magnet Linear Generator for Wave Energy Conversion," Energies, MDPI, vol. 15(7), pages 1-21, March.

    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:15:y:2022:i:21:p:8043-:d:957057. 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.