IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3443-d572789.html
   My bibliography  Save this article

A Current Sensor Fault Tolerant Control Strategy for PMSM Drive Systems Based on C ri Markers

Author

Listed:
  • Kamila Jankowska

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Mateusz Dybkowski

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

Abstract

The paper describes a vector-controlled fault tolerant control (FTC) structure for permanent magnet synchronous motor (PMSM) drives. As a control algorithm, the classical field oriented control was applied. For the proper operation of this drive, minimum two current sensors are necessary, however, in the FTC drives additional redundant transducers are applied. Each measuring sensor, including current sensors, are susceptible to damage and can lead to unstable operation of the drive. The presented control structure, with a diagnostic and compensation system, is robust to the typical current sensor faults—lack of signal, intermittent signal, variable gain, signal noise and signal saturation. The fault detection algorithm is based on the signal method. The fault diagnostic system is tested in two control algorithms—the scalar control and vector control ones—to demonstrate the transient of the faulted signals, detection signals and detection time. After current sensor fault appearance, its influence on the control structure, especially speed transient, is compensated using non-sensitive components. The analysis is presented for all the abovementioned faults for different speed conditions.

Suggested Citation

  • Kamila Jankowska & Mateusz Dybkowski, 2021. "A Current Sensor Fault Tolerant Control Strategy for PMSM Drive Systems Based on C ri Markers," Energies, MDPI, vol. 14(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3443-:d:572789
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3443/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3443/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahmoud A. Mossa & Hamdi Echeikh & Ahmed A. Zaki Diab & Hassan Haes Alhelou & Pierluigi Siano, 2021. "Comparative Study of Hysteresis Controller, Resonant Controller and Direct Torque Control of Five-Phase IM under Open-Phase Fault Operation," Energies, MDPI, vol. 14(5), pages 1-23, February.
    2. Pierpaolo Dini & Sergio Saponara, 2019. "Cogging Torque Reduction in Brushless Motors by a Nonlinear Control Technique," Energies, MDPI, vol. 12(11), pages 1-20, June.
    3. Pierpaolo Dini & Sergio Saponara, 2020. "Design of an Observer-Based Architecture and Non-Linear Control Algorithm for Cogging Torque Reduction in Synchronous Motors," Energies, MDPI, vol. 13(8), pages 1-20, April.
    4. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    5. Przemyslaw Pietrzak & Marcin Wolkiewicz, 2021. "Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents," Energies, MDPI, vol. 14(6), pages 1-23, March.
    6. Pawel Ewert, 2020. "The Application of the Bispectrum Analysis to Detect the Rotor Unbalance of the Induction Motor Supplied by the Mains and Frequency Converter," Energies, MDPI, vol. 13(11), pages 1-18, June.
    7. Pawel Ewert & Teresa Orlowska-Kowalska & Kamila Jankowska, 2021. "Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks," Energies, MDPI, vol. 14(3), pages 1-24, 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. Maciej Skowron & Krystian Teler & Michal Adamczyk & Teresa Orlowska-Kowalska, 2022. "Classification of Single Current Sensor Failures in Fault-Tolerant Induction Motor Drive Using Neural Network Approach," Energies, MDPI, vol. 15(18), pages 1-17, September.
    2. Kamran Naseri & Mai The Vu & Saleh Mobayen & Amin Najafi & Afef Fekih, 2022. "Design of Linear Matrix Inequality-Based Adaptive Barrier Global Sliding Mode Fault Tolerant Control for Uncertain Systems with Faulty Actuators," Mathematics, MDPI, vol. 10(13), pages 1-14, June.
    3. Zhengmeng Liu & Wenxuan Li & Guohai Liu, 2023. "A Novel Three-Layer Symmetry Winding Configuration for Five-Phase Motor," Energies, MDPI, vol. 16(2), pages 1-11, January.

    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. Cinzia Bernardeschi & Pierpaolo Dini & Andrea Domenici & Maurizio Palmieri & Sergio Saponara, 2020. "Formal Verification and Co-Simulation in the Design of a Synchronous Motor Control Algorithm," Energies, MDPI, vol. 13(16), pages 1-23, August.
    2. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.
    3. Pierpaolo Dini & Sergio Saponara, 2022. "Review on Model Based Design of Advanced Control Algorithms for Cogging Torque Reduction in Power Drive Systems," Energies, MDPI, vol. 15(23), pages 1-29, November.
    4. Rafael de Farias Campos & Cesar da Silva Liberato & José de Oliveira & Tiago Jackson May Dezuo & Ademir Nied, 2022. "Dynamic Strategy for Effective Current Reduction in Brushless DC Synchronous Motors Fault Tolerant Operation," Energies, MDPI, vol. 15(24), pages 1-17, December.
    5. T. A. Anuja & M. Arun Noyal Doss, 2021. "Reduction of Cogging Torque in Surface Mounted Permanent Magnet Brushless DC Motor by Adapting Rotor Magnetic Displacement," Energies, MDPI, vol. 14(10), pages 1-20, May.
    6. Josue A. Reyes-Malanche & Francisco J. Villalobos-Pina & Efraın Ramırez-Velasco & Eduardo Cabal-Yepez & Geovanni Hernandez-Gomez & Misael Lopez-Ramirez, 2023. "Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic," Energies, MDPI, vol. 16(1), pages 1-15, January.
    7. Hisahide Nakamura & Keisuke Asano & Seiran Usuda & Yukio Mizuno, 2021. "A Diagnosis Method of Bearing and Stator Fault in Motor Using Rotating Sound Based on Deep Learning," Energies, MDPI, vol. 14(5), pages 1-15, March.
    8. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.
    9. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    10. Pierpaolo Dini & Sergio Saponara, 2020. "Design of an Observer-Based Architecture and Non-Linear Control Algorithm for Cogging Torque Reduction in Synchronous Motors," Energies, MDPI, vol. 13(8), pages 1-20, April.
    11. Przemyslaw Pietrzak & Marcin Wolkiewicz, 2021. "Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents," Energies, MDPI, vol. 14(6), pages 1-23, March.
    12. Pierpaolo Dini & Sergio Saponara, 2020. "Design of Adaptive Controller Exploiting Learning Concepts Applied to a BLDC-Based Drive System," Energies, MDPI, vol. 13(10), pages 1-20, May.
    13. Maciej Skowron & Czeslaw T. Kowalski & Teresa Orlowska-Kowalska, 2022. "Impact of the Convolutional Neural Network Structure and Training Parameters on the Effectiveness of the Diagnostic Systems of Modern AC Motor Drives," Energies, MDPI, vol. 15(19), pages 1-22, September.
    14. Lucian Mihet-Popa & Sergio Saponara, 2021. "Power Converters, Electric Drives and Energy Storage Systems for Electrified Transportation and Smart Grid Applications," Energies, MDPI, vol. 14(14), pages 1-5, July.
    15. Abderrahman El Idrissi & Aziz Derouich & Said Mahfoud & Najib El Ouanjli & Ahmed Chantoufi & Ameena Saad Al-Sumaiti & Mahmoud A. Mossa, 2022. "Bearing Fault Diagnosis for an Induction Motor Controlled by an Artificial Neural Network—Direct Torque Control Using the Hilbert Transform," Mathematics, MDPI, vol. 10(22), pages 1-32, November.
    16. Khaled Farag & Abdullah Shawier & Ayman S. Abdel-Khalik & Mohamed M. Ahmed & Shehab Ahmed, 2021. "Applicability Analysis of Indices-Based Fault Detection Technique of Six-Phase Induction Motor," Energies, MDPI, vol. 14(18), pages 1-23, September.
    17. Federico Gargiulo & Annalisa Liccardo & Rosario Schiano Lo Moriello, 2022. "A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors," Energies, MDPI, vol. 15(12), pages 1-19, June.
    18. 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.
    19. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    20. Habib Benbouhenni & Nicu Bizon, 2021. "Improved Rotor Flux and Torque Control Based on the Third-Order Sliding Mode Scheme Applied to the Asynchronous Generator for the Single-Rotor Wind Turbine," Mathematics, MDPI, vol. 9(18), pages 1-16, September.

    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:14:y:2021:i:12:p:3443-:d:572789. 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.