Paper Info
Title
Online Inductance Identification Using PWM Current Ripple for Position Sensorless Drive of High-Speed Surface-Mounted Permanent Magnet Synchronous Machines
Abstract
Back–electromotive force estimation-based rotor position estimation methods are usually adopted for high-speed surface-mounted permanent magnet synchronous machines (SPMSM) drive to deal with the limitations of position sensors in high-speed applications. Considering inductance mismatch is the main cause of the position estimation error, this article proposes a novel pulsewidth modulation (PWM) current ripple-based online inductance identification method to improve the position estimation accuracy. The proposed method utilizes the inherent PWM current ripple of the voltage source inverter for inductance identification. In addition, the transient circuit equations on the estimated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gamma \delta$</tex-math></inline-formula> frame are adopted as the identification model, and the recursive least squares method is used to obtain the estimated inductance. Compared with the traditional parameter identification methods, the proposed method does not need to inject additional signals or fix parameters. During the sensorless drive of high-speed SPMSM, the proposed method can accurately identify the inductance while not affecting the normal operation of PMSM at all, which is the main contribution of this article. With the proposed method, the estimated inductance can rapidly converge to the accurate value, and then the position estimation error will be eliminated. Finally, the effectiveness of the proposed method is verified by simulations and experiments.
Year
DOI
Venue
2022
10.1109/TIE.2021.3130327
IEEE Transactions on Industrial Electronics
Keywords
DocType
Volume
Current ripple,high-speed surface-mounted permanent magnet synchronous machines (SPMSM),online inductance identification,position sensorless drive
Journal
69
Issue
ISSN
Citations 
12
0278-0046
0
PageRank 
References 
Authors
0.34
10
5
Name
Order
Citations
PageRank
Jindong Zhang100.34
Fei Peng236038.79
Yunkai Huang312.75
Yao Yu47822.67
Zichong Zhu500.34