IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i19p7997-d420468.html
   My bibliography  Save this article

Reduced-Complexity Model Predictive Control with Online Parameter Assessment for a Grid-Connected Single-Phase Multilevel Inverter

Author

Listed:
  • Ibrahim Harbi

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Menoufia University, Shebin El-Koum 32511, Egypt)

  • Mohamed Abdelrahem

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

  • Mostafa Ahmed

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

  • Ralph Kennel

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany)

Abstract

This paper proposes a finite control set model predictive control (FCS-MPC) with a reduced computational burden for a single-phase grid-connected modified packed U-cell multilevel inverter (MPUC-MLI) with two control objectives: reference current tracking and switching frequency minimization. The considered competitive topology consists of two units with six active switches and two DC sources in each unit, allowing the generation of 49 levels in the output voltage, which is considered a significant reduction in the active and passive components compared to the conventional and recently developed topologies of multilevel inverters (MLIs). This topology has 49 different switching states, which means that 49 predictions of the future current and 49 calculations of the cost function are required for each evaluation of the conventional FCS-MPC. Accordingly, the computational load is heavy. Thus, this paper presents two reduced-complexity FCS-MPC methods to reduce the calculation burden. The first technique reduces the computational load almost to half by computing the reference voltage and dividing the states of the MLI into two sets. Based on the reference voltage polarity, one set is defined and evaluated to specify the optimal state, which has a minimal cost function. However, in the second proposed method, only three states of the 49 states are evaluated each iteration, achieving a significant reduction in the execution time and superior control performance compared to the conventional FCS-MPC. A mathematical analysis is conducted based on the reference voltage value to locate the three vectors under evaluation. In the second part of the paper, the sensitivity to parameter variations for the proposed simplified FCS-MPC is investigated and tackled by employing an extended Kalman filter (EKF). In addition, noise related to variable measurement is filtered in the proposed system with the EKF. The simulation investigation was performed using MATLAB/Simulink to validate the system under different operating conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7997-:d:420468
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/19/7997/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/19/7997/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mostafa Ahmed & Mohamed Abdelrahem & Ralph Kennel, 2020. "Highly Efficient and Robust Grid Connected Photovoltaic System Based Model Predictive Control with Kalman Filtering Capability," Sustainability, MDPI, vol. 12(11), pages 1-22, June.
    2. Roh Chan & Sangshin Kwak, 2018. "Improved Finite-Control-Set Model Predictive Control for Cascaded H-Bridge Inverters," Energies, MDPI, vol. 11(2), pages 1-27, February.
    3. Amir Taghvaie & Ahmad Alijani & M. Ebrahim Adabi & Mohammad Rezanejad & Jafar Adabi & Kumars Rouzbehi & Edris Pouresmaeil, 2019. "An Asymmetrical Step-Up Multilevel Inverter Based on Switched-Capacitor Network," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    4. 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.
    5. Ahmed Farhan & Mohamed Abdelrahem & Amr Saleh & Adel Shaltout & Ralph Kennel, 2020. "Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation," Energies, MDPI, vol. 13(2), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohamed Derbeli & Asma Charaabi & Oscar Barambones & Cristian Napole, 2021. "High-Performance Tracking for Proton Exchange Membrane Fuel Cell System PEMFC Using Model Predictive Control," Mathematics, MDPI, vol. 9(11), pages 1-17, May.
    2. Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.
    3. Yuanzhe Zhao & Linjie Ren & Zhiming Liao & Guobin Lin, 2021. "A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor," Energies, MDPI, vol. 14(8), pages 1-18, April.
    4. Jin Zhu & Tongzhen Wei & Ming Ma & Libo Han, 2019. "Simple DC-Link Voltage Balancing Approach for Cascaded H-Bridge Rectifier with Asymmetric Parameters of Independent DC Loads," Energies, MDPI, vol. 12(9), pages 1-20, April.
    5. 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.
    6. Cheng-Kai Lin & Jen-te Yu & Hao-Qun Huang & Jyun-Ting Wang & Hsing-Cheng Yu & Yen-Shin Lai, 2018. "A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems," Energies, MDPI, vol. 11(7), pages 1-29, July.
    7. Leonardo Comparatore & Magno Ayala & Yassine Kali & Jorge Rodas & Julio Pacher & Alfredo Renault & Raúl Gregor, 2023. "Discrete-Time Sliding Mode Current Control for a Seven-Level Cascade H-Bridge Converter," Energies, MDPI, vol. 16(5), pages 1-19, March.
    8. Zhilin Lyu & Qing Wei & Yiyi Zhang & Junhui Zhao & Emad Manla, 2018. "Adaptive Virtual Impedance Droop Control Based on Consensus Control of Reactive Current," Energies, MDPI, vol. 11(7), pages 1-17, July.
    9. Muhammed Y. Worku & Mohamed A. Hassan & Luqman S. Maraaba & Md Shafiullah & Mohamed R. Elkadeem & Md Ismail Hossain & Mohamed A. Abido, 2023. "A Comprehensive Review of Recent Maximum Power Point Tracking Techniques for Photovoltaic Systems under Partial Shading," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
    10. 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.
    11. Yaqi Wang & Zhigang Liu, 2018. "Suppression Research Regarding Low-Frequency Oscillation in the Vehicle-Grid Coupling System Using Model-Based Predictive Current Control," Energies, MDPI, vol. 11(7), pages 1-21, July.
    12. 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.
    13. Mostafa Ahmed & Ibrahim Harbi & Ralph Kennel & José Rodríguez & Mohamed Abdelrahem, 2022. "Evaluation of the Main Control Strategies for Grid-Connected PV Systems," Sustainability, MDPI, vol. 14(18), pages 1-20, September.
    14. Daliang Yang & Li Yin & Shengguang Xu & Ning Wu, 2018. "Power and Voltage Control for Single-Phase Cascaded H-Bridge Multilevel Converters under Unbalanced Loads," Energies, MDPI, vol. 11(9), pages 1-18, September.
    15. Ersan Kabalci & Aydin Boyar, 2022. "Highly Efficient Interleaved Solar Converter Controlled with Extended Kalman Filter MPPT," Energies, MDPI, vol. 15(21), pages 1-24, October.
    16. Mohamed Abdelrahem & José Rodríguez & Ralph Kennel, 2020. "Improved Direct Model Predictive Control for Grid-Connected Power Converters," Energies, MDPI, vol. 13(10), pages 1-14, May.
    17. Chengwei Luo & Derong Luo & Shoudao Huang & Gongping Wu & Hongzhang Zhu & Qianjun He, 2018. "A Novel Control Strategy for DC-Link Voltage Balance and Reactive Power Equilibrium of a Single-Phase Cascaded H-Bridge Rectifier," Energies, MDPI, vol. 12(1), pages 1-20, December.
    18. Waqas Anjum & Abdul Rashid Husain & Junaidi Abdul Aziz & Syed Muhammad Fasih ur Rehman & Muhammad Paend Bakht & Hasan Alqaraghuli, 2022. "A Robust Dynamic Control Strategy for Standalone PV System under Variable Load and Environmental Conditions," Sustainability, MDPI, vol. 14(8), pages 1-27, April.
    19. Xiang Li & Zhenya Ji & Fengkun Yang & Zhenlan Dou & Chunyan Zhang & Liangliang Chen, 2022. "A Distributed Two-Level Control Strategy for DC Microgrid Considering Safety of Charging Equipment," Energies, MDPI, vol. 15(22), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7997-:d:420468. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.