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A Fast Reconfiguration Technique for Boost-Based DMPPT PV Systems Based on Deterministic Clustering Analysis

Author

Listed:
  • Marco Balato

    (Department of Electrical and Information Technologies, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Carlo Petrarca

    (Department of Electrical and Information Technologies, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Annalisa Liccardo

    (Department of Electrical and Information Technologies, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Martina Botti

    (Department of Electrical and Information Technologies, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
    These authors contributed equally to this work.)

  • Luigi Verolino

    (Department of Electrical and Information Technologies, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
    These authors contributed equally to this work.)

Abstract

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving.

Suggested Citation

  • Marco Balato & Carlo Petrarca & Annalisa Liccardo & Martina Botti & Luigi Verolino, 2023. "A Fast Reconfiguration Technique for Boost-Based DMPPT PV Systems Based on Deterministic Clustering Analysis," Energies, MDPI, vol. 16(23), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7882-:d:1292825
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    References listed on IDEAS

    as
    1. Daraban, Stefan & Petreus, Dorin & Morel, Cristina, 2014. "A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading," Energy, Elsevier, vol. 74(C), pages 374-388.
    2. Bahgat, A.B.G. & Helwa, N.H. & Ahmad, G.E. & El Shenawy, E.T., 2005. "Maximum power point traking controller for PV systems using neural networks," Renewable Energy, Elsevier, vol. 30(8), pages 1257-1268.
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    4. Ishaque, Kashif & Salam, Zainal & Lauss, George, 2014. "The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions," Applied Energy, Elsevier, vol. 119(C), pages 228-236.
    5. 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.
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