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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

Author

Listed:
  • Muhammad Awais

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
    These authors contributed equally to this work.)

  • Laiq Khan

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
    These authors contributed equally to this work.)

  • Said Ghani Khan

    (Department of Mechanical Engineering, College of Engineering, University of Bahrain, Isa Town 32038, Bahrain)

  • Qasim Awais

    (Department of Electronics and Computer Science, Fatima Jinnah Women University, Rawalpindi, Old Presidency, Rawalpindi 46000, 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 stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1902-:d:1068502
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    References listed on IDEAS

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    1. Marko Vekić & Milan Rapaić & Ivana Todorović & Stevan Grabić, 2024. "Decentralized Goal-Function-Based Microgrid Primary Control with Voltage Harmonics Compensation," Energies, MDPI, vol. 17(19), pages 1-18, October.
    2. Anuradha Chandrasekar & Vijayalakshmi Subramanian & Narayanamoorthi Rajamanickam & Mohammad Shorfuzzaman & Ahmed Emara, 2024. "Design and Control of Four-Port Non-Isolated SEPIC Converter for Hybrid Renewable Energy Systems," Sustainability, MDPI, vol. 16(19), pages 1-24, September.
    3. Matej Tkac & Martina Kajanova & Peter Bracinik, 2023. "A Review of Advanced Control Strategies of Microgrids with Charging Stations," Energies, MDPI, vol. 16(18), pages 1-25, September.

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