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Artificial Intelligence-Based Weighting Factor Autotuning for Model Predictive Control of Grid-Tied Packed U-Cell Inverter

Author

Listed:
  • Mostefa Mohamed-Seghir

    (Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland)

  • Abdelbasset Krama

    (LEVRES Laboratory, The University of El-Oued, Fac. Technology, El-Oued 39000, Algeria
    Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar)

  • Shady S. Refaat

    (Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar)

  • Mohamed Trabelsi

    (Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar
    Department of Electronic and Communications Engineering, Kuwait College of Science and Technology, Doha, P.O. Box 27235, Kuwait)

  • Haitham Abu-Rub

    (Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Qatar Foundation, Doha, PO Box 23874, Qatar)

Abstract

The tuning of weighting factor has been considered as the most challenging task in the implementation of multi-objective model predictive control (MPC) techniques. Thus, this paper proposes an artificial intelligence (AI)-based weighting factor autotuning in the design of a finite control set MPC (FCS-MPC) applied to a grid-tied seven-level packed U-cell (PUC7) multilevel inverter (MLI). The studied topology is capable of producing a seven-level output voltage waveform and inject sinusoidal current to the grid with high power quality while using a reduced number of components. The proposed cost function optimization algorithm ensures auto-adjustment of the weighting factor to guarantee low injected grid current total harmonic distortion (THD) at different power ratings while balancing the capacitor voltage. The optimal weighting factor value is selected at each sampling time to guarantee a stable operation of the PUC inverter with high power quality. The weighting factor selection is performed using an artificial neural network (ANN) based on the measured injected grid current. Simulation and experimental results are presented to show the high performance of the proposed strategy in handling multi-objective control problems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3107-:d:372144
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    References listed on IDEAS

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    1. Abdelbasset Krama & Laid Zellouma & Boualaga Rabhi & Shady S. Refaat & Mansour Bouzidi, 2018. "Real-Time Implementation of High Performance Control Scheme for Grid-Tied PV System for Power Quality Enhancement Based on MPPC-SVM Optimized by PSO Algorithm," Energies, MDPI, vol. 11(12), pages 1-26, December.
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    Cited by:

    1. Piotr Szewczyk & Andrzej Łebkowski, 2021. "Studies on Energy Consumption of Electric Light Commercial Vehicle Powered by In-Wheel Drive Modules," Energies, MDPI, vol. 14(22), pages 1-28, November.
    2. 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.
    3. Andrzej Łebkowski & Wojciech Koznowski, 2020. "Analysis of the Use of Electric and Hybrid Drives on SWATH Ships," Energies, MDPI, vol. 13(24), pages 1-26, December.
    4. Ibrahim Harbi & Mohamed Abdelrahem & Mostafa Ahmed & Ralph Kennel, 2020. "Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter," Sustainability, MDPI, vol. 12(19), pages 1-23, September.

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