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A Virtual Impedance-Based Flying Start Considering Transient Characteristics for Permanent Magnet Synchronous Machine Drive Systems

Author

Listed:
  • Yoon-Seong Lee

    (Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Kyoung-Min Choo

    (Electric Propulsion Research Center, Korea Electrotechnology Research Institute (KERI), Changwon 51541, Republic of Korea)

  • Won-Sang Jeong

    (Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Chang-Hee Lee

    (Dawonsys Co., Ltd., Anyang 15655, Republic of Korea)

  • Junsin Yi

    (Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Chung-Yuen Won

    (Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

Abstract

A virtual impedance-based flying start considering transient characteristics for permanent magnet synchronous machine drive systems is proposed. The conventional flying start based on virtual resistance (VR) assumes that the load of the system is resistive. However, the maximum value of VR, which is determined by the machine parameter and sampling frequency, is sometimes small. In this case, the load of the system is non-resistive. This assumption error causes an estimated position error and degrades transient characteristics. In the proposed method, algebraic-type virtual inductance (VI) is added to the estimation current regulator of the flying start based on VR. This change improves the accuracy of the estimated rotor position and the transient characteristics. In addition, the discrete-time system model of the proposed flying start method is given, the stability was analyzed considering the change in VR caused by the proposed method, and the improvements were verified by PSIM simulations and experimental results.

Suggested Citation

  • Yoon-Seong Lee & Kyoung-Min Choo & Won-Sang Jeong & Chang-Hee Lee & Junsin Yi & Chung-Yuen Won, 2023. "A Virtual Impedance-Based Flying Start Considering Transient Characteristics for Permanent Magnet Synchronous Machine Drive Systems," Energies, MDPI, vol. 16(3), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1172-:d:1042850
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    References listed on IDEAS

    as
    1. Ming-Shyan Wang & Tse-Ming Tsai, 2017. "Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement," Energies, MDPI, vol. 10(11), pages 1-15, November.
    2. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(6), pages 1-30, March.
    3. 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.
    4. Fardila Mohd Zaihidee & Saad Mekhilef & Marizan Mubin, 2019. "Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review," Energies, MDPI, vol. 12(9), pages 1-27, May.
    5. Maha Zoghlami & Ameni Kadri & Faouzi Bacha, 2018. "Analysis and Application of the Sliding Mode Control Approach in the Variable-Wind Speed Conversion System for the Utility of Grid Connection," Energies, MDPI, vol. 11(4), pages 1-17, March.
    6. Mingcheng Lyu & Gongping Wu & Derong Luo & Fei Rong & Shoudao Huang, 2019. "Robust Nonlinear Predictive Current Control Techniques for PMSM," Energies, MDPI, vol. 12(3), pages 1-19, January.
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