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Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm

Author

Listed:
  • Efrain Mendez

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico)

  • Alexandro Ortiz

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico)

  • Pedro Ponce

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico)

  • Israel Macias

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico)

  • David Balderas

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico)

  • Arturo Molina

    (Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 14380, Mexico)

Abstract

Nowadays, owing to the growing interest in renewable energy, Photovoltaic systems (PV) are responsible of supplying more than 500,000 GW of the electrical energy consumed around the world. Therefore, different converters topologies, control algorithms, and techniques have been studied and developed in order to maximize the energy harvested by PV sources. Maximum Power Point Tracking (MPPT) methods are usually employed with DC/DC converters, which together are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage supplied in order to achieve a dynamic optimization of the transferred energy. MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence, but with a compromised relation between the settling time and steady-state oscillations, leading to a trade off between them. Nevertheless, proposed methods like Particle Swarm Optimization- (PSO) based techniques have improved the settling time with the addition of lower steady-state oscillations. Yet, such a proposal performance is highly susceptible and dependent to correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA) that enables a solution with an easy parameters calibration and an improved dynamic behavior. Hence, a boost converter case study is proposed to verify the suitability of the proposed technique through Simscape Power Systems™ simulations, regarding the dynamic model fidelity capabilities of the software. Results show that the proposed structure can easily be suited into different power applications. The proposed solution, reduced between 12% and 36% the energy wasted in the simulation compared to the P&O and PSO based proposals.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3047-:d:370795
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2023. "A Fuzzy Logic Control for Maximum Power Point Tracking Algorithm Validated in a Commercial PV System," Energies, MDPI, vol. 16(2), pages 1-14, January.
    3. 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.
    4. 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.
    5. Dilip Kumar & Yogesh Kumar Chauhan & Ajay Shekhar Pandey & Ankit Kumar Srivastava & Varun Kumar & Faisal Alsaif & Rajvikram Madurai Elavarasan & Md Rabiul Islam & Raju Kannadasan & Mohammed H. Alshari, 2023. "A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization," Sustainability, MDPI, vol. 15(6), pages 1-29, March.
    6. 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.
    7. 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.
    8. 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).

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