IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i12p3089-d371876.html
   My bibliography  Save this article

High Resistance Fault-Detection and Fault-Tolerance for Asymmetrical Six-Phase Surface-Mounted AC Permanent Magnet Synchronous Motor Drives

Author

Listed:
  • Claudio Rossi

    (DEI—Department of Electrical, Electronic and Information Engineering—”Guglielmo Marconi”, University of Bologna, 40126 Bologna, Italy)

  • Yasser Gritli

    (DEI—Department of Electrical, Electronic and Information Engineering—”Guglielmo Marconi”, University of Bologna, 40126 Bologna, Italy
    DEE—Department of Electrical Engineering, LR-11-ES18, National Engineering School of Tunis, University of Tunis El Manar, Tunis, Belvedere 1002, Tunisia)

  • Alessio Pilati

    (DEI—Department of Electrical, Electronic and Information Engineering—”Guglielmo Marconi”, University of Bologna, 40126 Bologna, Italy)

  • Gabriele Rizzoli

    (DEI—Department of Electrical, Electronic and Information Engineering—”Guglielmo Marconi”, University of Bologna, 40126 Bologna, Italy)

  • Angelo Tani

    (DEI—Department of Electrical, Electronic and Information Engineering—”Guglielmo Marconi”, University of Bologna, 40126 Bologna, Italy)

  • Domenico Casadei

    (DEI—Department of Electrical, Electronic and Information Engineering—”Guglielmo Marconi”, University of Bologna, 40126 Bologna, Italy)

Abstract

In the last decade, the interest for higher reliability in several industrial applications has boosted the research activities in multiphase permanent magnet synchronous motors realized by multiple three-phase winding sets. In this study, a mathematical model of an asymmetric surface-mounted six-phase permanent magnet synchronous motor under high resistance connections was developed. By exploiting the intrinsic properties of multiphase machines in terms of degrees of freedom, an improved field-oriented control scheme is presented that allows online fault detection and a quite undisturbed operating condition of the machine under high resistance connections. More specifically, the proposed strategies for online fault-detection and fault-tolerance are based on the use of multi-reference frame current regulators. The feasibility of the proposed approach was theoretically analyzed, then confirmed by numerical simulations. In order to validate experimentally the proposed strategies, the entire control system was implemented using TMS-320F2812 based platform.

Suggested Citation

  • Claudio Rossi & Yasser Gritli & Alessio Pilati & Gabriele Rizzoli & Angelo Tani & Domenico Casadei, 2020. "High Resistance Fault-Detection and Fault-Tolerance for Asymmetrical Six-Phase Surface-Mounted AC Permanent Magnet Synchronous Motor Drives," Energies, MDPI, vol. 13(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3089-:d:371876
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/12/3089/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/12/3089/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pedro Gonçalves & Sérgio Cruz & André Mendes, 2019. "Finite Control Set Model Predictive Control of Six-Phase Asymmetrical Machines—An Overview," Energies, MDPI, vol. 12(24), pages 1-42, December.
    2. Yuri Merizalde & Luis Hernández-Callejo & Oscar Duque-Perez, 2017. "State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors," Energies, MDPI, vol. 10(7), pages 1-34, July.
    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. Marwa Ben Slimene & Mohamed Arbi Khlifi, 2022. "Investigation on the Effects of Magnetic Saturation in Six-Phase Induction Machines with and without Cross Saturation of the Main Flux Path," Energies, MDPI, vol. 15(24), pages 1-18, December.
    2. Karol Wróbel & Piotr Serkies & Krzysztof Szabat, 2020. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches," Energies, MDPI, vol. 13(5), pages 1-15, March.
    3. Sebastian Berhausen & Tomasz Jarek, 2021. "Method of Limiting Shaft Voltages in AC Electric Machines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    4. Mitja Nemec & Vanja Ambrožič & Rastko Fišer & David Nedeljković & Klemen Drobnič, 2019. "Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring," Energies, MDPI, vol. 12(5), pages 1-17, February.
    5. Jaime A. Rohten & David N. Dewar & Pericle Zanchetta & Andrea Formentini & Javier A. Muñoz & Carlos R. Baier & José J. Silva, 2021. "Multivariable Deadbeat Control of Power Electronics Converters with Fast Dynamic Response and Fixed Switching Frequency," Energies, MDPI, vol. 14(2), pages 1-16, January.
    6. Piotr Kołodziejek & Daniel Wachowiak, 2022. "Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive," Energies, MDPI, vol. 15(3), pages 1-14, February.
    7. Camila Paes Salomon & Wilson Cesar Sant’Ana & Germano Lambert-Torres & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda De Oliveira, 2018. "Comparison among Methods for Induction Motor Low-Intrusive Efficiency Evaluation Including a New AGT Approach with a Modified Stator Resistance," Energies, MDPI, vol. 11(4), pages 1-21, March.
    8. Carlos Romero & Larizza Delorme & Osvaldo Gonzalez & Magno Ayala & Jorge Rodas & Raul Gregor, 2021. "Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines," Energies, MDPI, vol. 14(13), pages 1-15, June.
    9. Muhammad Zuhaib & Faraz Ahmed Shaikh & Wajiha Tanweer & Abdullah M. Alnajim & Saleh Alyahya & Sheroz Khan & Muhammad Usman & Muhammad Islam & Mohammad Kamrul Hasan, 2022. "Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment," Energies, MDPI, vol. 15(21), pages 1-32, October.
    10. Arkadiusz Duda & Maciej Sułowicz, 2020. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms," Energies, MDPI, vol. 13(14), pages 1-26, July.
    11. Angel Arranz-Gimon & Angel Zorita-Lamadrid & Daniel Morinigo-Sotelo & Oscar Duque-Perez, 2021. "A Review of Total Harmonic Distortion Factors for the Measurement of Harmonic and Interharmonic Pollution in Modern Power Systems," Energies, MDPI, vol. 14(20), pages 1-38, October.
    12. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    13. Mateusz Dybkowski & Szymon Antoni Bednarz, 2019. "Modified Rotor Flux Estimators for Stator-Fault-Tolerant Vector Controlled Induction Motor Drives," Energies, MDPI, vol. 12(17), pages 1-21, August.
    14. Martin Kuchar & Petr Palacky & Petr Simonik & Jan Strossa, 2021. "Self-Tuning Observer for Sensor Fault-Tolerant Control of Induction Motor Drive," Energies, MDPI, vol. 14(9), pages 1-16, April.
    15. Yuriy Zhukovskiy & Aleksandra Buldysko & Ilia Revin, 2023. "Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current," Energies, MDPI, vol. 16(8), pages 1-23, April.
    16. Zabdur Rehman & Kwanjae Seong, 2018. "Three-D Numerical Thermal Analysis of Electric Motor with Cooling Jacket," Energies, MDPI, vol. 11(1), pages 1-15, January.
    17. Sergio Toledo & Edgar Maqueda & Marco Rivera & Raúl Gregor & Pat Wheeler & Carlos Romero, 2020. "Improved Predictive Control in Multi-Modular Matrix Converter for Six-Phase Generation Systems," Energies, MDPI, vol. 13(10), pages 1-13, May.
    18. Arkadiusz Duda & Piotr Drozdowski, 2020. "Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis," Energies, MDPI, vol. 13(24), pages 1-25, December.
    19. Yatai Ji & Paolo Giangrande & Vincenzo Madonna & Weiduo Zhao & Michael Galea, 2021. "Reliability-Oriented Design of Inverter-Fed Low-Voltage Electrical Machines: Potential Solutions," Energies, MDPI, vol. 14(14), pages 1-25, July.
    20. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.

    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:13:y:2020:i:12:p:3089-:d:371876. 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.