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Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study

Author

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  • Somashree Pathy

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu 603203, India)

  • C. Subramani

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu 603203, India)

  • R. Sridhar

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu 603203, India)

  • T. M. Thamizh Thentral

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu 603203, India)

  • Sanjeevikumar Padmanaban

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

Abstract

PV generating sources are one of the most promising power generation systems in today’s power scenario. The inherent potential barrier that PV possesses with respect to irradiation and temperature is its nonlinear power output characteristics. An intelligent power tracking scheme, e.g., maximum power point tracking (MPPT), is mandatorily employed to increase the power delivery of a PV system. The MPPT schemes experiences severe setbacks when the PV is even shaded partially as PV exhibits multiple power peaks. Therefore, the search mechanism gets deceived and gets stuck with the local maxima. Hence, a rational search mechanism should be developed, which will find the global maxima for a partially shaded PV. The conventional techniques like fractional open circuit voltage (FOCV), hill climbing (HC) method, perturb and observe (P&O), etc., even in their modified versions, are not competent enough to track the global MPP (GMPP). Nature-inspired and bio-inspired MPPT techniques have been proposed by the researchers to optimize the power output of a PV system during partially shaded conditions (PSCs). This paper reviews, compares, and analyzes them. This article renders firsthand information to those in the field of research, who seek interest in the performance enhancement of PV system during inhomogeneous irradiation. Each algorithm has its own advantages and disadvantages in terms of convergence speed, coding complexity, hardware compatibility, stability, etc. Overall, the authors have presented the logic of each global search MPPT algorithms and its comparisons, and also have reviewed the performance enhancement of these techniques when these algorithms are hybridized.

Suggested Citation

  • Somashree Pathy & C. Subramani & R. Sridhar & T. M. Thamizh Thentral & Sanjeevikumar Padmanaban, 2019. "Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study," Energies, MDPI, vol. 12(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1451-:d:223345
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    References listed on IDEAS

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    Cited by:

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    4. Mehdi Seyedmahmoudian & Gokul Sidarth Thirunavukkarasu & Elmira Jamei & Tey Kok Soon & Ben Horan & Saad Mekhilef & Alex Stojcevski, 2020. "A Sustainable Distributed Building Integrated Photo-Voltaic System Architecture with a Single Radial Movement Optimization Based MPPT Controller," Sustainability, MDPI, vol. 12(16), pages 1-21, August.
    5. Tamir Shaqarin, 2023. "Particle Swarm Optimization with Targeted Position-Mutated Elitism (PSO-TPME) for Partially Shaded PV Systems," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    6. CH Hussaian Basha & C Rani, 2020. "Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
    7. Amit Kumar Sharma & Rupendra Kumar Pachauri & Sushabhan Choudhury & Ahmad Faiz Minai & Majed A. Alotaibi & Hasmat Malik & Fausto Pedro García Márquez, 2023. "Role of Metaheuristic Approaches for Implementation of Integrated MPPT-PV Systems: A Comprehensive Study," Mathematics, MDPI, vol. 11(2), pages 1-48, January.
    8. Shahzad Ahmed & Hafiz Mian Muhammad Adil & Iftikhar Ahmad & Muhammad Kashif Azeem & Zil e Huma & Safdar Abbas Khan, 2020. "Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a Solar PV System with Artificial Neural Networks Based Reference Generation," Energies, MDPI, vol. 13(14), pages 1-24, July.
    9. Luigi Costanzo & Massimo Vitelli, 2019. "A Novel MPPT Technique for Single Stage Grid-Connected PV Systems: T4S," Energies, MDPI, vol. 12(23), pages 1-13, November.
    10. Rajvikram Madurai Elavarasan & Aritra Ghosh & Tapas K. Mallick & Apoorva Krishnamurthy & Meenal Saravanan, 2019. "Investigations on Performance Enhancement Measures of the Bidirectional Converter in PV–Wind Interconnected Microgrid System," Energies, MDPI, vol. 12(14), pages 1-22, July.
    11. M. Bani Salim & H. S. Hayajneh & A. Mohammed & S. Ozcelik, 2019. "Robust Direct Adaptive Controller Design for Photovoltaic Maximum Power Point Tracking Application," Energies, MDPI, vol. 12(16), pages 1-19, August.

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