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Robust Integral Backstepping Based Nonlinear MPPT Control for a PV System

Author

Listed:
  • Kamran Ali

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

  • Laiq Khan

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

  • Qudrat Khan

    (Center for Advanced Studies in Telecommunication, COMSATS University Islamabad, Islamabad 45550, Pakistan)

  • Shafaat Ullah

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

  • Saghir Ahmad

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

  • Sidra Mumtaz

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

  • Fazal Wahab Karam

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

  • Naghmash

    (School of Electrical Engineering, Shandong University, Jinan 250000, China)

Abstract

A photovoltaic system generates energy that depends on the environmental conditions such as temperature, irradiance and the variations in the load connected to it. To adapt to the consistently increasing interest of energy, the photovoltaic (PV) system must operate at maximum power point (MPP), however, it has the issue of low efficiency because of the varying climatic conditions. To increase its efficiency, a maximum power point technique is required to extract maximum power from the PV system. In this paper, a nonlinear fast and efficient maximum power point tracking (MPPT) technique is developed based on the robust integral backstepping (RIB) approach to harvest maximum power from a PV array using non-inverting DC-DC buck-boost converter. The study uses a NeuroFuzzy network to generate the reference voltage for MPPT. Asymptotic stability of the whole system is verified using Lyapunov stability criteria. The MATLAB/Simulink platform is used to test the proposed controller performance under varying meteorological conditions. The simulation results validate that the proposed controller effectively improves the MPPT in terms of tracking speed and efficiency. For further validation of the proposed controller performance, a comparative study is presented with backstepping controller, integral backstepping, robust backstepping and conventional MPPT algorithms (PID and P&O) under rapidly varying environmental conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3180-:d:258950
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    References listed on IDEAS

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    1. Wang, Zhaohua & Li, Yi & Wang, Ke & Huang, Zhimin, 2017. "Environment-adjusted operational performance evaluation of solar photovoltaic power plants: A three stage efficiency analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 1153-1162.
    2. 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.
    3. Lyden, S. & Haque, M.E., 2015. "Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1504-1518.
    4. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
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    Cited by:

    1. Miaomiao Ma & Xiangjie Liu & Kwang Y. Lee, 2020. "Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Lilia Tightiz & Saeedeh Mansouri & Farhad Zishan & Joon Yoo & Nima Shafaghatian, 2022. "Maximum Power Point Tracking for Photovoltaic Systems Operating under Partially Shaded Conditions Using SALP Swarm Algorithm," Energies, MDPI, vol. 15(21), pages 1-17, November.
    3. 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.
    4. Zaheer Alam & Qudrat Khan & Laiq Khan & Safeer Ullah & Syed Abdul Mannan Kirmani & Abdullah A. Algethami, 2022. "Certainty-Equivalence-Based Sensorless Robust Sliding Mode Control for Maximum Power Extraction of an Uncertain Photovoltaic System," Energies, MDPI, vol. 15(6), pages 1-17, March.
    5. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.
    6. Xue Lin & Lixia Sun & Ping Ju & Hongyu Li, 2019. "Stochastic Control for Intra-Region Probability Maximization of Multi-Machine Power Systems Based on the Quasi-Generalized Hamiltonian Theory," Energies, MDPI, vol. 13(1), pages 1-16, December.
    7. Omer Saleem & Shehryaar Ali & Jamshed Iqbal, 2023. "Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller," Energies, MDPI, vol. 16(13), pages 1-20, June.

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