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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Hyeon-Seok Lee & Jae-Jung Yun, 2019. "Advanced MPPT Algorithm for Distributed Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-17, September.
    4. 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.
    5. 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.
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