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Enhancing MPPT Performance in Partially Shaded PV Systems under Sensor Malfunctioning with Fuzzy Control

Author

Listed:
  • Moazzam Ali Rabbani

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan)

  • Muhammad Bilal Qureshi

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan)

  • Salman A. Al Qahtani

    (Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia)

  • Muhammad Mohsin Khan

    (Department of IT & CS, FECID, PAF-IAST, Haripur 22620, Pakistan)

  • Pranavkumar Pathak

    (School of Continuing Studies, McGill University, Montreal, QC H3A 0G4, Canada)

Abstract

The shift towards sustainable energy sources is gaining momentum due to their environmental cleanliness, abundant availability, and eco-friendly characteristics. Solar energy, specifically harnessed through photovoltaic (PV) systems, emerges as a clean, abundant, and environmentally friendly alternative. However, the efficacy of PV systems is subjective depending on two critical factors: irradiance and temperature. To optimize power output, maximum power point tracking (MPPT) strategies are essential, allowing operation at the system’s optimal point. In the presence of partial shading, the power–voltage curve exhibits multiple peaks, yet only one global maximum power point (GMPP) can be identified. Existing algorithms for GMPP tracking often encounter challenges, including overshooting during transient periods and chattering during steady states. This study proposes the utilization of fuzzy sliding mode controllers (FSMC) and fuzzy proportional-integral (FPI) control to enhance global MPPT reference tracking under partial shading conditions. Additionally, the system’s performance is evaluated considering potential sensor malfunctions. The proposed techniques ensure precise tracking of the reference voltage and maximum power in partial shading scenarios, facilitating rapid convergence, improved system stability during transitions, and reduced chattering during steady states. The usefulness of the proposed scheme is confirmed through the use of performance indices. FSMC has the lowest integral absolute error (IAE) of 946.94, followed closely by FPI (947.21), in comparison to the sliding mode controller (SMC) (1241.6) and perturb and observe (P&O) (2433.1). Similarly, in integral time absolute error (ITAE), FSMC (56.84) and FPI (57.06) excel over SMC (91.03) and P&O (635.50).

Suggested Citation

  • Moazzam Ali Rabbani & Muhammad Bilal Qureshi & Salman A. Al Qahtani & Muhammad Mohsin Khan & Pranavkumar Pathak, 2023. "Enhancing MPPT Performance in Partially Shaded PV Systems under Sensor Malfunctioning with Fuzzy Control," Energies, MDPI, vol. 16(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4665-:d:1169424
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    References listed on IDEAS

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    2. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
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    Cited by:

    1. Giovanny Chavez & Luis Tipán, 2024. "Maximum Power Transfer of a Photovoltaic Microgeneration System Using PSO-Based Dynamic Modeling," Energies, MDPI, vol. 17(15), pages 1-26, July.

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