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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

Author

Listed:
  • Abdelbasset Krama

    (Electrical Engineering Department, LEVRES Laboratory, El -Oued University, El-Oued 39000, Algeria
    Electrical & Computer Engineering Department, Texas A&M University at Qatar, P.O. Box 23874, Education City, 77874 Doha, Qatar)

  • Laid Zellouma

    (Electrical Engineering Department, LEVRES Laboratory, El -Oued University, El-Oued 39000, Algeria)

  • Boualaga Rabhi

    (Electrical Engineering Department, LMSE Laboratory, Biskra University, Biskra 07000, Algeria)

  • Shady S. Refaat

    (Electrical & Computer Engineering Department, Texas A&M University at Qatar, P.O. Box 23874, Education City, 77874 Doha, Qatar)

  • Mansour Bouzidi

    (Département de l’Electronique et des Communications, Faculté des Nouvelles Technologies d’Information et Communication, Université Kasdi Merbah, Ouargla 30000, Algeria)

Abstract

This paper proposes a high performance control scheme for a double function grid-tied double-stage PV system. It is based on model predictive power control with space vector modulation. This strategy uses a discrete model of the system based on the time domain to generate the average voltage vector at each sampling period, with the aim of canceling the errors between the estimated active and reactive power values and their references. Also, it imposes a sinusoidal waveform of the current at the grid side, which allows active power filtering without a harmonic currents identification phase. The latter attempts to reduce the size and cost of the system as well as providing better performance. In addition, it can be implemented in a low-cost control platform due to its simplicity. A double-stage PV system is selected due to its flexibility in control, unlike single-stage strategies. Sliding mode control-based particle swarm optimization (PSO) is used to track the maximum power of the PV system. It offers high accuracy and good robustness. Concerning DC bus voltage of the inverter, the anti-windup PI controller is tuned offline using the particle swarm optimization algorithm to deliver optimal performance in DC bus voltage regulation. The overall system has been designed and validated in an experimental prototype; the obtained results in different phases demonstrate the higher performance and the better efficiency of the proposed system in terms of power quality enhancement and PV power injection.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3516-:d:191196
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    References listed on IDEAS

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    8. Neeraj Priyadarshi & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Farooque Azam, 2018. "Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm," Energies, MDPI, vol. 11(5), pages 1-16, April.
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    Cited by:

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    2. Abderahmane Abid & Abualkasim Bakeer & Laid Zellouma & Mansour Bouzidi & Abderezak Lashab & Boualaga Rabhi, 2023. "Low Computational Burden Predictive Direct Power Control of Quasi Z-Source Inverter for Grid-Tied PV Applications," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    3. Daniel Gutierrez-Reina & Federico Barrero & Jose Riveros & Ignacio Gonzalez-Prieto & Sergio L. Toral & Mario J. Duran, 2019. "Interest and Applicability of Meta-Heuristic Algorithms in the Electrical Parameter Identification of Multiphase Machines," Energies, MDPI, vol. 12(2), pages 1-15, January.
    4. Emiyamrew Minaye Molla & Cheng-Chien Kuo, 2020. "Voltage Quality Enhancement of Grid-Integrated PV System Using Battery-Based Dynamic Voltage Restorer," Energies, MDPI, vol. 13(21), pages 1-16, November.
    5. 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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