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A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution

Author

Listed:
  • Yi Jin

    (Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230036, China)

  • Wenhui Hou

    (Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230036, China)

  • Guiqiang Li

    (Department of Thermal Science and Energy Engineering, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China)

  • Xiao Chen

    (State Key Laboratory of Fire Science, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China)

Abstract

The output power of a photovoltaic (PV) system depends on the external solar irradiation and its own temperature. In order to obtain more power from the PV system, the maximum power point tracking (MPPT) is necessary. However, when the PV is partially shaded, there will be multiple peaks in the power-current (P-I) curve. The conventional MPPT methods may be invalid due to falling into the local peak. In addition, in a photovoltaic-thermal (PV/T) system, the non-uniform temperature distribution on PV will also occur, which complicates the situation. This paper presents a MPPT method with glowworm swarm optimization (GSO) for PV in a PV/T system under non-uniform solar irradiation and temperature distribution. In order to study the performance of the proposed method, the conventional methods including the perturbation and observe algorithm (P and O), and the fractional open-circuit voltage technique (FOCVT) are compared with it in this paper. Simulation results show that the proposed method can rapidly track the real maximum power point (MPP) under different conditions, such as the gradient temperature distribution, the fast variable solar irradiation and the variable partial shading. The outcome indicates the proposed method has obvious advantages, especially the performance being superior to the conventional methods under the partial shading condition.

Suggested Citation

  • Yi Jin & Wenhui Hou & Guiqiang Li & Xiao Chen, 2017. "A Glowworm Swarm Optimization-Based Maximum Power Point Tracking for Photovoltaic/Thermal Systems under Non-Uniform Solar Irradiation and Temperature Distribution," Energies, MDPI, vol. 10(4), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:541-:d:95920
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    References listed on IDEAS

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    Cited by:

    1. Sai Krishna, G. & Moger, Tukaram, 2019. "Improved SuDoKu reconfiguration technique for total-cross-tied PV array to enhance maximum power under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 333-348.
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    3. Pi-Yun Chen & Kuei-Hsiang Chao & Bo-Jyun Liao, 2018. "Joint Operation between a PSO-Based Global MPP Tracker and a PV Module Array Configuration Strategy under Shaded or Malfunctioning Conditions," Energies, MDPI, vol. 11(8), pages 1-16, August.
    4. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    5. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.
    6. Eduardo Manuel Godinho Rodrigues & Radu Godina & Mousa Marzband & Edris Pouresmaeil, 2018. "Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity," Energies, MDPI, vol. 11(11), pages 1-21, October.
    7. 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.

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