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Feedback-Linearization-Based Fuel-Cell Adaptive-Control Paradigm in a Microgrid Using a Wavelet-Entrenched NeuroFuzzy Framework

Author

Listed:
  • Muhammad Awais

    (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, Islamabad 45550, Pakistan)

  • Saghir Ahmad

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

  • Mohsin Jamil

    (Department of Electrical and Computer Engineering, Faculty of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

Abstract

The article portrays an adaptive control paradigm for the swift response of a solid-oxide fuel cell (SOFC) in a grid-connected microgrid. The control scheme is based on an adaptive feedback-linearization-embedded fully recurrent NeuroFuzzy Laguerre wavelet control (FBL-FRNF-Lag-WC) framework. The nonlinear functions of feedback linearization (FBL) are estimated using a fully recurrent NeuroFuzzy Laguerre wavelet control (FRNF-Lag-WC) architecture with a recurrent Gaussian membership function in the antecedent part and a recurrent Laguerre wavelet in the consequent part, respectively. The performance of the proposed control scheme is validated for various stability, quality, and reliability factors obtained through a simulation testbed implemented in MATLAB/Simulink. The proposed scheme is compared against adaptive NeuroFuzzy, PID, and adaptive PID (aPID) control schemes using different performance parameters for a grid-connected load over 24 h.

Suggested Citation

  • Muhammad Awais & Laiq Khan & Saghir Ahmad & Mohsin Jamil, 2021. "Feedback-Linearization-Based Fuel-Cell Adaptive-Control Paradigm in a Microgrid Using a Wavelet-Entrenched NeuroFuzzy Framework," Energies, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1850-:d:524888
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    References listed on IDEAS

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    1. Shivashankar Sukumar & Marayati Marsadek & Agileswari Ramasamy & Hazlie Mokhlis & Saad Mekhilef, 2017. "A Fuzzy-Based PI Controller for Power Management of a Grid-Connected PV-SOFC Hybrid System," Energies, MDPI, vol. 10(11), pages 1-17, October.
    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.
    3. Yonghui Li & Qiuwei Wu & Haiyu Zhu, 2015. "Hierarchical Load Tracking Control of a Grid-Connected Solid Oxide Fuel Cell for Maximum Electrical Efficiency Operation," Energies, MDPI, vol. 8(3), pages 1-21, March.
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    Cited by:

    1. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    2. Rasool Kahani & Mohsin Jamil & M. Tariq Iqbal, 2022. "Direct Model Reference Adaptive Control of a Boost Converter for Voltage Regulation in Microgrids," Energies, MDPI, vol. 15(14), pages 1-19, July.
    3. Muhammad Awais & Laiq Khan & Said Ghani Khan & Qasim Awais & Mohsin Jamil, 2023. "Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid," Energies, MDPI, vol. 16(4), pages 1-40, February.

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