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Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter

Author

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  • Maria I. S. Guerra

    (Department of Engineering and Technology, Semi-Arid Federal University, Mossoró 59625-900, Brazil)

  • Fábio M. Ugulino de Araújo

    (Department of Computer and Automation Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil)

  • Mahmoud Dhimish

    (Department of Electronic Engineering, University of York, York YO10 5DD, UK)

  • Romênia G. Vieira

    (Department of Engineering and Technology, Semi-Arid Federal University, Mossoró 59625-900, Brazil)

Abstract

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.

Suggested Citation

  • Maria I. S. Guerra & Fábio M. Ugulino de Araújo & Mahmoud Dhimish & Romênia G. Vieira, 2021. "Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter," Energies, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7453-:d:674822
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    References listed on IDEAS

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    1. Reshma Gopi, R. & Sreejith, S., 2018. "Converter topologies in photovoltaic applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 1-14.
    2. Romênia G. Vieira & Fábio M. U. de Araújo & Mahmoud Dhimish & Maria I. S. Guerra, 2020. "A Comprehensive Review on Bypass Diode Application on Photovoltaic Modules," Energies, MDPI, vol. 13(10), pages 1-21, May.
    3. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
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    5. Jaw-Kuen Shiau & Min-Yi Lee & Yu-Chen Wei & Bo-Chih Chen, 2014. "Circuit Simulation for Solar Power Maximum Power Point Tracking with Different Buck-Boost Converter Topologies," Energies, MDPI, vol. 7(8), pages 1-20, August.
    6. Shazly A. Mohamed & Mohamed A. Tolba & Ayman A. Eisa & Ali M. El-Rifaie, 2021. "Comprehensive Modeling and Control of Grid-Connected Hybrid Energy Sources Using MPPT Controller," Energies, MDPI, vol. 14(16), pages 1-22, August.
    7. 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.
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