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RBF neural network based backstepping terminal sliding mode MPPT control technique for PV system

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  • Zain Ahmad Khan
  • Laiq Khan
  • Saghir Ahmad
  • Sidra Mumtaz
  • Muhammad Jafar
  • Qudrat Khan

Abstract

The energy demand in the world has increased rapidly in the last few decades. This demand is arising the need for alternative energy resources. Solar energy is the most eminent energy resource which is completely free from pollution and fuel. However, the problem occurs when it comes to efficiency under different atmospheric conditions such as varying temperature and solar irradiance. To achieve its maximum efficiency, an algorithm of maximum power point tracking (MPPT) is needed to fetch maximum power from the photovoltaic (PV) system. In this article, a nonlinear backstepping terminal sliding mode control (BTSMC) is proposed for maximum power extraction. The system is finite-time stable and its stability is validated through the Lyapunov function. A DC-DC buck-boost converter is used to deliver PV power to the load. For the proposed controller, reference voltages are generated by a radial basis function neural network (RBF NN). The proposed controller performance is tested using the MATLAB/Simulink tool. Furthermore, the controller performance is compared with the perturb and observe (P&O) MPPT algorithm, Proportional Integral Derivative (PID) controller and backstepping MPPT nonlinear controller. The results validate that the proposed controller offers better tracking and fast convergence in finite time under rapidly varying conditions of the environment.

Suggested Citation

  • Zain Ahmad Khan & Laiq Khan & Saghir Ahmad & Sidra Mumtaz & Muhammad Jafar & Qudrat Khan, 2021. "RBF neural network based backstepping terminal sliding mode MPPT control technique for PV system," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    • Handle: RePEc:plo:pone00:0249705
    DOI: 10.1371/journal.pone.0249705
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    References listed on IDEAS

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    1. Belhachat, Faiza & Larbes, Cherif, 2018. "A review of global maximum power point tracking techniques of photovoltaic system under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 513-553.
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    Cited by:

    1. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.

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