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Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

Author

Listed:
  • Ahmed Farhan

    (Institute for Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 München, Germany
    Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Mohamed Abdelrahem

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

  • Amr Saleh

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Adel Shaltout

    (Electrical Engineering Department, Faculty of Engineering, Cairo University, Cairo 12613, Egypt)

  • Ralph Kennel

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

Abstract

In this paper, a simplified efficient method for sensorless finite set current predictive control (FSCPC) for synchronous reluctance motor (SynRM) based on extended Kalman filter (EKF) is proposed. The proposed FSCPC is based on reducing the computation burden of the conventional FSCPC by using the commanded reference currents to directly calculate the reference voltage vector (RVV). Therefore, the cost function is calculated for only three times and the necessity to test all possible voltage vectors will be avoided. For sensorless control, EKF is composed to estimate the position and speed of the rotor. Whereas the performance of the proposed FSCPC essentially necessitates the full knowledge of SynRM parameters and provides an insufficient response under the parameter mismatch between the controller and the motor, online parameter estimation based on EKF is combined in the proposed control strategy to estimate all parameters of the machine. Furthermore, for simplicity, the parameters of PI speed controller and initial values of EKF covariance matrices are tuned offline using Particle Swarm Optimization (PSO). To demonstrate the feasibility of the proposed control, it is implemented in MATLAB/Simulink and tested under different operating conditions. Simulation results show high robustness and reliability of the proposed drive.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:492-:d:310748
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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