IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5594-d1201985.html
   My bibliography  Save this article

Comparative Study of Passivity, Model Predictive, and Passivity-Based Model Predictive Controllers in Uninterruptible Power Supply Applications

Author

Listed:
  • Shafquat Hussain

    (Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy)

  • Abualkasim Bakeer

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Ihab S. Mohamed

    (Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA)

  • Mario Marchesoni

    (Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy)

  • Luis Vaccaro

    (Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy)

Abstract

Voltage source converters are widely used in distributed generation (DG) and uninterruptible power supply (UPS) applications. This paper aims to find the controller that performs best when model changes occur in the system, showing insensitivity to parameter variations. A comparison of the finite control set model predictive controller (FCS-MPC), interconnection and damping assignment passivity-based controller (IDA-PBC), and passivity-based model predictive control (PB-MPC) reveals that the PB-MPC provides high resistance to these unexpected LC filter changes in the converter. The second aim of the paper is to reduce the total harmonic distortion (THD) of the output voltage of the three-phase voltage source inverter (VSI). A high total harmonic distortion (THD) value exists in the voltage waveform of the three-phase voltage source inverter (VSI), feeding a non-linear load. A MATLAB simulation was performed using three control techniques for a three-phase VSI feeding: linear load, unbalanced load, and non-linear load. The PB-MPC performs better than the FCS-MPC and IDA-PBC in terms of having a low THD value in the output voltage of the converter under all types of applied loads, improving the THD by up to 30%, and having low variation in THD with mismatched filter parameters, as shown in the bar charts in the results section. Overall, the PB-MPC controller improves the robustness under parameter mismatch and reduces the computational burden. PB-MPC reduces the THD value because it integrates power shaping and the injection of damping resistances into the VSI.

Suggested Citation

  • Shafquat Hussain & Abualkasim Bakeer & Ihab S. Mohamed & Mario Marchesoni & Luis Vaccaro, 2023. "Comparative Study of Passivity, Model Predictive, and Passivity-Based Model Predictive Controllers in Uninterruptible Power Supply Applications," Energies, MDPI, vol. 16(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5594-:d:1201985
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5594/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5594/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nickolay I. Shchurov & Sergey V. Myatezh & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergei I. Dedov, 2021. "Determination of Inactive Powers in a Single-Phase AC Network," Energies, MDPI, vol. 14(16), pages 1-13, August.
    Full references (including those not matched with items on IDEAS)

    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. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
    2. Yuriy Leonidovich Zhukovskiy & Margarita Sergeevna Kovalchuk & Daria Evgenievna Batueva & Nikita Dmitrievich Senchilo, 2021. "Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    3. Boris V. Malozyomov & Nikita V. Martyushev & Elena V. Voitovich & Roman V. Kononenko & Vladimir Yu. Konyukhov & Vadim Tynchenko & Viktor Alekseevich Kukartsev & Yadviga Aleksandrovna Tynchenko, 2023. "Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment," Energies, MDPI, vol. 16(13), pages 1-30, June.
    4. Helmo K. Morales Paredes & Matheus Branco Arcadepani & Alexandre Candido Moreira & Flávio A. Serrão Gonçalves & Fernando Pinhabel Marafão, 2023. "Enlightening Load Modeling by Means of Power Factor Decompositions," Energies, MDPI, vol. 16(10), pages 1-22, May.
    5. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    6. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    7. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    8. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    9. Boris V. Malozyomov & Nikita V. Martyushev & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Sergei Vasilievich Tynchenko & Roman V. Klyuev & Nikolay A. Zagorodnii & Yadviga Aleksandr, 2023. "Study of Supercapacitors Built in the Start-Up System of the Main Diesel Locomotive," Energies, MDPI, vol. 16(9), pages 1-24, May.
    10. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.

    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:jeners:v:16:y:2023:i:15:p:5594-:d:1201985. 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.