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Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Author

Listed:
  • Sanaz Sabzevari

    (Department of Electrical and Computer Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Rasool Heydari

    (Energy Technology Department, Aalborg University of Denmark, 9220 Aalborg, Denmark)

  • Maryam Mohiti

    (Department of Electrical Engineering, University of Yazd, Yazd 89158-18411, Iran)

  • Mehdi Savaghebi

    (Department of Mechanical and Electrical Engineering, University of Southern Denmark, 5230 Odense, Denmark)

  • Jose Rodriguez

    (Department of Engineering Science, Universidad Andres Bello, 7500971 Santiago, Chile)

Abstract

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

Suggested Citation

  • Sanaz Sabzevari & Rasool Heydari & Maryam Mohiti & Mehdi Savaghebi & Jose Rodriguez, 2021. "Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters," Energies, MDPI, vol. 14(8), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2325-:d:539691
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    References listed on IDEAS

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    1. Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    2. Mostefa Mohamed-Seghir & Abdelbasset Krama & Shady S. Refaat & Mohamed Trabelsi & Haitham Abu-Rub, 2020. "Artificial Intelligence-Based Weighting Factor Autotuning for Model Predictive Control of Grid-Tied Packed U-Cell Inverter," Energies, MDPI, vol. 13(12), pages 1-14, June.
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    Cited by:

    1. Constantin Volosencu, 2022. "Study of the Angular Positioning of a Rotating Object Based on Some Computational Intelligence Methods," Mathematics, MDPI, vol. 10(7), pages 1-46, April.
    2. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    3. Yuzhe Zhang & Xiaodong Liu & Haitao Li & Zhenbin Zhang, 2023. "A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables," Energies, MDPI, vol. 16(9), pages 1-15, April.
    4. M. A. Hannan & Ali Q. Al-Shetwi & M. S. Mollik & Pin Jern Ker & M. Mannan & M. Mansor & Hussein M. K. Al-Masri & T. M. Indra Mahlia, 2023. "Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions," Sustainability, MDPI, vol. 15(5), pages 1-30, February.
    5. Muhammad Nauman & Wajiha Shireen & Amir Hussain, 2022. "Model-Free Predictive Control and Its Applications," Energies, MDPI, vol. 15(14), pages 1-24, July.
    6. Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.

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