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Robust Differentiator-Based NeuroFuzzy Sliding Mode Control Strategies for PMSG-WECS

Author

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  • Malak Adnan Khan

    (Department of Electronics Engineering, Abbotabad Campus, University of Engineering and Technology, Peshawar 22020, Pakistan)

  • Qudrat Khan

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

  • Laiq Khan

    (Department of Electrical Engineering, COMSATS University, Islamabad 45550, Pakistan)

  • Imran Khan

    (Department of Electrical Engineering, College of Engineering and Technology, University of Sargodha, Sargodha 40100, Pakistan)

  • Ahmad Aziz Alahmadi

    (Department of Electrical Engineering, Faculty of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Nasim Ullah

    (Department of Electrical Engineering, Faculty of Engineering, Taif University, Taif 21944, Saudi Arabia)

Abstract

A robust control algorithm is always needed to harvest maximum power from a Wind Energy Conversion System (WECS) by operating it consistently at a Maximum Power Point (MPP) in the presence of wind speed variations. In this work, a Maximum Power Point Tracking (MPPT) control algorithm is designed via Conventional Sliding Mode Control (CSMC), the Super Twisting Algorithm (STA), and the Real Twisting Algorithm (RTA) and is applied to a Permanent Magnet Synchronous Generator (PMSG)-based WECS. CSMC is model-based whereas the STA and RTA are model-free controllers. In practice, the unavailability of nonlinear terms and aerodynamic forces deteriorates the performance of these controllers. Thus, an offline NeuroFuzzy algorithm is incorporated to estimate the nonlinear drift and control input channel to improve the robustness of these algorithms. In addition, the generator shaft speed and its missing derivative is recovered via a Uniform Robust Exact Differentiator (URED). In order to carry out a comprehensive comparative study among the three competitors, the overall system is simulated in a closed loop under the action of these controllers at three different operating conditions, i.e., nominal, varying load and inertia, and varying wind speed, using MATLAB/Simulink. The acquired results confirm the superiority of the RTA over the STA and CSMC in terms of robustness and chatter reduction.

Suggested Citation

  • Malak Adnan Khan & Qudrat Khan & Laiq Khan & Imran Khan & Ahmad Aziz Alahmadi & Nasim Ullah, 2022. "Robust Differentiator-Based NeuroFuzzy Sliding Mode Control Strategies for PMSG-WECS," Energies, MDPI, vol. 15(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7039-:d:924731
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    References listed on IDEAS

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    1. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
    2. Baroudi, Jamal A. & Dinavahi, Venkata & Knight, Andrew M., 2007. "A review of power converter topologies for wind generators," Renewable Energy, Elsevier, vol. 32(14), pages 2369-2385.
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