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Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems

Author

Listed:
  • Izhar Ul Haq
  • Qudrat Khan
  • Safeer Ullah
  • Shahid Ahmed Khan
  • Rini Akmeliawati
  • Mehmood Ashraf Khan
  • Jamshed Iqbal

Abstract

The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation.

Suggested Citation

  • Izhar Ul Haq & Qudrat Khan & Safeer Ullah & Shahid Ahmed Khan & Rini Akmeliawati & Mehmood Ashraf Khan & Jamshed Iqbal, 2022. "Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    • Handle: RePEc:plo:pone00:0260480
    DOI: 10.1371/journal.pone.0260480
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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. 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.
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    1. Nazari, Mostafa & Darvishi Nejad, Hossein & Mohammadzadeh, Ardashir & Zhang, Chunwei, 2024. "Multi-variable fuzzy adaptive time-varying sliding mode control (MVFATVSMC) of the reverse osmosis-photovoltaic desalination system," Energy, Elsevier, vol. 309(C).

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