Position Offset and Current Angle Combined Injection Based online Multiparameter Estimation of PMSM Considering Inverter Distortion

To address the rank deficiency issue of multiparameter estimation for permanent magnet synchronous motors (PMSMs), this paper introduces a method based on combined injection of position offset and current angle offset. Specifically, the opposite position offset and current angle offset are injected into the machine for data collection. During the injection, the dq-axis current magnitudes remain unchanged to avoid the variation of inductances and inverter distorted voltage, which can improve the estimation accuracy. This paper combines least squares method with variable forgetting factors for online estimation of multiparameter from the collected data. Both experimental and simulation verification confirm that the proposed approach is effective for both surface-mounted and interior PMSMs and leads to less torque and speed fluctuations during online parameter estimation.

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Published
2024-11-26
DOI
https://doi.org/10.23919/icems60997.2024.10920977
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Position Offset and Current Angle Combined Injection Based online Multiparameter Estimation of PMSM Considering Inverter Distortion

Zhe Tong, Guodong Feng, Yanli Pei
Iterative Learning Control Systems
article

Position Offset and Current Angle Combined Injection Based online Multiparameter Estimation of PMSM Considering Inverter Distortion

Zhe Tong, Guodong Feng, Yanli Pei
article en

Abstract

To address the rank deficiency issue of multiparameter estimation for permanent magnet synchronous motors (PMSMs), this paper introduces a method based on combined injection of position offset and current angle offset. Specifically, the opposite position offset and current angle offset are injected into the machine for data collection. During the injection, the dq-axis current magnitudes remain unchanged to avoid the variation of inductances and inverter distorted voltage, which can improve the estimation accuracy. This paper combines least squares method with variable forgetting factors for online estimation of multiparameter from the collected data. Both experimental and simulation verification confirm that the proposed approach is effective for both surface-mounted and interior PMSMs and leads to less torque and speed fluctuations during online parameter estimation.

Sun Yat-sen University (CN)
National Natural Science Foundation of China, Guangdong Science and Technology Department
Affordable and clean energy
Openalex Percentile: Top 64%
Iterative Learning Control Systems
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