IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8134-d694976.html
   My bibliography  Save this article

Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks

Author

Listed:
  • Konrad Urbanski

    (Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland)

  • Dariusz Janiszewski

    (Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

This paper presents a method for shaft position estimation of a synchronous motor with permanent magnets. Zero speed and very low speed range are considered. The method uses the analysis of high-frequency currents induced by the introduction of additional voltage in the control path in the stationary coordinate system associated with the stator. An artificial neural network estimates the sine and cosine values necessary in the Park’s transformation units. This method can achieve satisfactory accuracy in the case of low asymmetry of inductance in the direct and quadrature axes of the coordinate system associated with the rotor. The TensorFlow/Keras package was used for artificial network calculations and the scikit-learn package for preprocessing. Aggregating the outputs of several artificial neural networks provides an opportunity to reduce the resultant estimation error. The use of as few as four networks has enabled the error to be reduced by approximately 20% compared to a single example network.

Suggested Citation

  • Konrad Urbanski & Dariusz Janiszewski, 2021. "Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks," Energies, MDPI, vol. 14(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8134-:d:694976
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8134/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/8134/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaime Pando-Acedo & Enrique Romero-Cadaval & Maria Isabel Milanes-Montero & Fermin Barrero-Gonzalez, 2020. "Improvements on a Sensorless Scheme for a Surface-Mounted Permanent Magnet Synchronous Motor Using Very Low Voltage Injection," Energies, MDPI, vol. 13(11), pages 1-17, May.
    2. Krzysztof Szabat & Karol Wróbel & Krzysztof Dróżdż & Dariusz Janiszewski & Tomasz Pajchrowski & Adrian Wójcik, 2020. "A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint," Energies, MDPI, vol. 13(8), pages 1-18, April.
    3. Malinowski, M. & Kazmierkowski, M.P. & Trzynadlowski, A., 2003. "Review and comparative study of control techniques for three-phase PWM rectifiers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 63(3), pages 349-361.
    4. Hanying Gao & Wen Zhang & Yu Wang & Zhuo Chen, 2019. "Fault-Tolerant Control Strategy for 12-Phase Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 12(18), pages 1-17, September.
    5. Andrzej Łebkowski, 2018. "Design, Analysis of the Location and Materials of Neodymium Magnets on the Torque and Power of In-Wheel External Rotor PMSM for Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-23, August.
    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. Alessandro Benevieri & Lorenzo Carbone & Simone Cosso & Krishneel Kumar & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2022. "Surface Permanent Magnet Synchronous Motors’ Passive Sensorless Control: A Review," Energies, MDPI, vol. 15(20), pages 1-26, October.

    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. Karol Wróbel & Kacper Śleszycki & Krzysztof Szabat & Seiichiro Katsura, 2021. "Application of Multilayer Observer for a Drive System with Flexibility," Energies, MDPI, vol. 14(24), pages 1-19, December.
    2. Yongda Li & Pingping Gong, 2023. "Fault-Tolerant Control of Induction Motor with Current Sensors Based on Dual-Torque Model," Energies, MDPI, vol. 16(8), pages 1-15, April.
    3. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    4. Karol Wróbel & Kacper Śleszycki & Amanuel Haftu Kahsay & Krzysztof Szabat & Seiichiro Katsura, 2023. "Robust Speed Control of Uncertain Two-Mass System," Energies, MDPI, vol. 16(17), pages 1-17, August.
    5. Piotr Dukalski & Roman Krok, 2021. "Selected Aspects of Decreasing Weight of Motor Dedicated to Wheel Hub Assembly by Increasing Number of Magnetic Poles," Energies, MDPI, vol. 14(4), pages 1-27, February.
    6. Xiaoyu Deng & Ruo Mo & Pengliang Wang & Junru Chen & Dongliang Nan & Muyang Liu, 2023. "Review of RoCoF Estimation Techniques for Low-Inertia Power Systems," Energies, MDPI, vol. 16(9), pages 1-19, April.
    7. Krzysztof Dmitruk, 2024. "A Simplified Guide to Control Algorithms for Grid Converters in Renewable Energy Systems," Energies, MDPI, vol. 17(18), pages 1-18, September.
    8. Dominik Łuczak, 2021. "Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part," Energies, MDPI, vol. 14(21), pages 1-12, November.
    9. Dingyu Wang & Yiguang Chen, 2020. "Fault-Tolerant Control of Coil Inter-Turn Short-Circuit in Five-Phase Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 13(21), pages 1-19, October.
    10. Naouar, M.W. & Ben Hania, B. & Slama-Belkhodja, I. & Monmasson, E. & Naassani, A.A., 2013. "FPGA-based sliding mode direct control of single phase PWM boost rectifier," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 249-261.
    11. Jingde Xia & Shaozhuo Li & Shuping Gao & Wenquan Shao & Guobing Song & Changjiang Chen, 2021. "Research on Differential Protection of Generator Based on New Braking Mode," Energies, MDPI, vol. 14(7), pages 1-16, March.
    12. T. Mesbahi & A. Ouari & T. Ghennam & E. M. Berkouk & N. Mesbahi, 2016. "A hybrid wind energy conversion system/active filter for non linear conditions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 1-8, December.
    13. Mustafa Tumbek & Selami Kesler, 2019. "Design and Implementation of a Low Power Outer-Rotor Line-Start Permanent-Magnet Synchronous Motor for Ultra-Light Electric Vehicles," Energies, MDPI, vol. 12(16), pages 1-20, August.
    14. Piotr Dukalski & Jan Mikoś & Roman Krok, 2022. "Analysis of the Simulation of the Operation of a Wheel Hub Motor Mounted in a Hybrid Drive of a Delivery Vehicle," Energies, MDPI, vol. 15(21), pages 1-39, November.
    15. Radosław Nalepa & Karol Najdek & Karol Wróbel & Krzysztof Szabat, 2020. "Application of D-Decomposition Technique to Selection of Controller Parameters for a Two-Mass Drive System," Energies, MDPI, vol. 13(24), pages 1-21, December.
    16. Lorenzo Carbone & Simone Cosso & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2021. "State-Space Approach for SPMSM Sensorless Passive Algorithm Tuning," Energies, MDPI, vol. 14(21), pages 1-11, November.
    17. Christian Aldrete-Maldonado & Ramon Ramirez-Villalobos & Luis N. Coria & Corina Plata-Ante, 2023. "Sensorless Scheme for Permanent-Magnet Synchronous Motors Susceptible to Time-Varying Load Torques," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    18. Massimo Caruso & Antonino Oscar Di Tommaso & Giuseppe Lisciandrello & Rosa Anna Mastromauro & Rosario Miceli & Claudio Nevoloso & Ciro Spataro & Marco Trapanese, 2020. "A General and Accurate Measurement Procedure for the Detection of Power Losses Variations in Permanent Magnet Synchronous Motor Drives," Energies, MDPI, vol. 13(21), pages 1-19, November.
    19. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    20. 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.

    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:23:p:8134-:d:694976. 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.