LAPSE:2023.15969
Published Article

LAPSE:2023.15969
Permanent Magnet Synchronous Motor Model Extension for High-Frequency Signal Injection-Based Sensorless Magnet Polarity Detection
March 2, 2023
Abstract
In this paper, a novel extended permanent magnet synchronous motor model is presented that incorporates a quadratic flux-current function to represent the polarity-dependent saliency. The proposed model enables the design of sensorless polarity detection algorithms required by the initial position detection of permanent magnet synchronous motors. The novelty of the model is that it integrates the polarity-dependent saliency into the traditional machine model and introduces a new machine parameter, the polarity-dependent saliency coefficient, to specify the Hessian matrix of the flux-current function. A measurement method is presented for determination of the elements of the Hessian and the polarity-dependent saliency coefficient. The solution of the model is given for high-frequency sinusoidal pulsating voltage injection. Experimental results show that the proposed extended model accurately predicts the amplitudes and phases of the second harmonics of the motor currents, which are the carriers of the polarity-dependent information. This information enables a current measurement-based polarity detection algorithm using the phase difference between the fundamental and second harmonic of the apparent d-axis current. Both the presented measurement data and the proposed model show that injection in the d-direction is optimal for polarity detection.
In this paper, a novel extended permanent magnet synchronous motor model is presented that incorporates a quadratic flux-current function to represent the polarity-dependent saliency. The proposed model enables the design of sensorless polarity detection algorithms required by the initial position detection of permanent magnet synchronous motors. The novelty of the model is that it integrates the polarity-dependent saliency into the traditional machine model and introduces a new machine parameter, the polarity-dependent saliency coefficient, to specify the Hessian matrix of the flux-current function. A measurement method is presented for determination of the elements of the Hessian and the polarity-dependent saliency coefficient. The solution of the model is given for high-frequency sinusoidal pulsating voltage injection. Experimental results show that the proposed extended model accurately predicts the amplitudes and phases of the second harmonics of the motor currents, which are the carriers of the polarity-dependent information. This information enables a current measurement-based polarity detection algorithm using the phase difference between the fundamental and second harmonic of the apparent d-axis current. Both the presented measurement data and the proposed model show that injection in the d-direction is optimal for polarity detection.
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Keywords
initial position detection, permanent magnet synchronous motor, polarity detection, saliency, sensorless control
Subject
Suggested Citation
Szalay I, Fodor D, Enisz K, Medve H. Permanent Magnet Synchronous Motor Model Extension for High-Frequency Signal Injection-Based Sensorless Magnet Polarity Detection. (2023). LAPSE:2023.15969
Author Affiliations
Szalay I: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Fodor D: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Enisz K: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Medve H: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Fodor D: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Enisz K: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Medve H: Vehicle Industry Research Center, Széchenyi István University, H-9026 Győr, Hungary [ORCID]
Journal Name
Energies
Volume
15
Issue
3
First Page
1131
Year
2022
Publication Date
2022-02-03
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15031131, Publication Type: Journal Article
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LAPSE:2023.15969
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https://doi.org/10.3390/en15031131
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Mar 2, 2023
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