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Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System

Author

Listed:
  • Sidra Mumtaz

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Saghir Ahmad

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Laiq Khan

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Saima Ali

    (Department of Electrical Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan)

  • Tariq Kamal

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Serdivan 54050, Sakarya, Turkey
    Department of Electrical Engineering, Higher Polytechnic School of Algeciras, University of Cadiz, 11202 Algeciras, Spain)

  • Syed Zulqadar Hassan

    (Department of Power System and Its Automation, Chongqing University, Chongqing 400044, China)

Abstract

In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT.

Suggested Citation

  • Sidra Mumtaz & Saghir Ahmad & Laiq Khan & Saima Ali & Tariq Kamal & Syed Zulqadar Hassan, 2018. "Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:606-:d:135524
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    References listed on IDEAS

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    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. Lalili, D. & Mellit, A. & Lourci, N. & Medjahed, B. & Berkouk, E.M., 2011. "Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter," Renewable Energy, Elsevier, vol. 36(12), pages 3282-3291.
    3. Syed Zulqadar Hassan & Hui Li & Tariq Kamal & Uğur Arifoğlu & Sidra Mumtaz & Laiq Khan, 2017. "Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    4. Sivakumar, P. & Abdul Kader, Abdullah & Kaliavaradhan, Yogeshraj & Arutchelvi, M., 2015. "Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions," Renewable Energy, Elsevier, vol. 81(C), pages 543-550.
    5. Sidra Mumtaz & Saima Ali & Saghir Ahmad & Laiq Khan & Syed Zulqadar Hassan & Tariq Kamal, 2017. "Energy Management and Control of Plug-In Hybrid Electric Vehicle Charging Stations in a Grid-Connected Hybrid Power System," Energies, MDPI, vol. 10(11), pages 1-21, November.
    6. Saghir Ahmad & Laiq Khan, 2017. "Performance Analysis of Conjugate Gradient Algorithms Applied to the Neuro-Fuzzy Feedback Linearization-Based Adaptive Control Paradigm for Multiple HVDC Links in AC/DC Power System," Energies, MDPI, vol. 10(6), pages 1-23, June.
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    Citations

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    Cited by:

    1. Kamran Ali & Laiq Khan & Qudrat Khan & Shafaat Ullah & Saghir Ahmad & Sidra Mumtaz & Fazal Wahab Karam & Naghmash, 2019. "Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System," Energies, MDPI, vol. 12(16), pages 1-20, August.
    2. Muhammad Hafeez Mohamed Hariri & Mohd Khairunaz Mat Desa & Syafrudin Masri & Muhammad Ammirrul Atiqi Mohd Zainuri, 2020. "Grid-Connected PV Generation System—Components and Challenges: A Review," Energies, MDPI, vol. 13(17), pages 1-28, August.
    3. Muhammad Awais & Laiq Khan & Saghir Ahmad & Sidra Mumtaz & Rabiah Badar, 2020. "Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-36, June.
    4. Myada Shadoul & Hassan Yousef & Rashid Al Abri & Amer Al-Hinai, 2021. "Adaptive Fuzzy Approximation Control of PV Grid-Connected Inverters," Energies, MDPI, vol. 14(4), pages 1-22, February.
    5. Carlos Restrepo & Nicolas Yanẽz-Monsalvez & Catalina González-Castaño & Samir Kouro & Jose Rodriguez, 2021. "A Fast Converging Hybrid MPPT Algorithm Based on ABC and P&O Techniques for a Partially Shaded PV System," Mathematics, MDPI, vol. 9(18), pages 1-25, September.

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