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Fast and Accurate Model of Interior Permanent-Magnet Machine for Dynamic Characterization

Author

Listed:
  • Klemen Drobnič

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

  • Lovrenc Gašparin

    (Mahle Electric Drives Slovenia, Polje 15, SI-5290 Šempeter pri Gorici, Slovenia)

  • Rastko Fišer

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, SI-1000 Ljubljana, Slovenia)

Abstract

A high-fidelity two-axis model of an interior permanent-magnet synchronous machine (IPM) presents a convenient way for the characterization and validation of motor dynamic performance during the design stage. In order to consider a nonlinear IPM nature, the model is parameterized with a standard dataset calculated beforehand by finite-element analysis. From two possible model implementations, the current model (CM) seems to be preferable to the flux-linkage model (FLM). A particular reason for this state of affairs is the rather complex and time-demanding parameterization of FLM in comparison with CM. For this reason, a procedure for the fast and reliable parameterization of FLM is presented. The proposed procedure is significantly faster than comparable methods, hence providing considerable improvement in terms of computational time. Additionally, the execution time of FLM was demonstrated to be up to 20% shorter in comparison to CM. Therefore, the FLM should be used in computationally intensive simulation scenarios that have a significant number of iterations, or excessive real-time time span.

Suggested Citation

  • Klemen Drobnič & Lovrenc Gašparin & Rastko Fišer, 2019. "Fast and Accurate Model of Interior Permanent-Magnet Machine for Dynamic Characterization," Energies, MDPI, vol. 12(5), pages 1-20, February.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:783-:d:209304
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    References listed on IDEAS

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    1. Yu-Xi Liu & Li-Yi Li & Ji-Wei Cao & Qin-He Gao & Zhi-Yin Sun & Jiang-Peng Zhang, 2018. "The Optimization Design of Short-Term High-Overload Permanent Magnet Motors Considering the Nonlinear Saturation," Energies, MDPI, vol. 11(12), pages 1-20, November.
    2. Xiaoyu Liu & Qifang Lin & Weinong Fu, 2017. "Optimal Design of Permanent Magnet Arrangement in Synchronous Motors," Energies, MDPI, vol. 10(11), pages 1-16, October.
    3. Chengming Zhang & Qingbo Guo & Liyi Li & Mingyi Wang & Tiecheng Wang, 2017. "System Efficiency Improvement for Electric Vehicles Adopting a Permanent Magnet Synchronous Motor Direct Drive System," Energies, MDPI, vol. 10(12), pages 1-27, December.
    4. Marcel Torrent & José Ignacio Perat & José Antonio Jiménez, 2018. "Permanent Magnet Synchronous Motor with Different Rotor Structures for Traction Motor in High Speed Trains," Energies, MDPI, vol. 11(6), pages 1-17, June.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Sandra Eriksson, 2019. "Permanent Magnet Synchronous Machines," Energies, MDPI, vol. 12(14), pages 1-5, July.
    2. Jonas Steffen & Sebastian Lengsfeld & Marco Jung & Bernd Ponick & Mercedes Herranz Gracia & Aristide Spagnolo & Markus Klöpzig & Klaus Schleicher & Klaus Schäfer, 2021. "Design of a Medium Voltage Generator with DC-Cascade for High Power Wind Energy Conversion Systems," Energies, MDPI, vol. 14(11), pages 1-17, May.
    3. Michał Michna & Filip Kutt & Łukasz Sienkiewicz & Roland Ryndzionek & Grzegorz Kostro & Dariusz Karkosiński & Bartłomiej Grochowski, 2020. "Mechanical-Level Hardware-In-The-Loop and Simulation in Validation Testing of Prototype Tower Crane Drives," Energies, MDPI, vol. 13(21), pages 1-25, November.
    4. Vasyl Varvolik & Shuo Wang & Dmytro Prystupa & Giampaolo Buticchi & Sergei Peresada & Michael Galea & Serhiy Bozhko, 2022. "Fast Experimental Magnetic Model Identification for Synchronous Reluctance Motor Drives," Energies, MDPI, vol. 15(6), pages 1-15, March.

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