Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157166
Title: Online adaptation of two-parameter inverter model in sensorless motor drives
Authors: Chen, Jiahao
Mei, Jie
Yuan, Xin
Zuo, Yuefei
Zhu, Jingwei
Lee, Christopher Ho Tin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Chen, J., Mei, J., Yuan, X., Zuo, Y., Zhu, J. & Lee, C. H. T. (2022). Online adaptation of two-parameter inverter model in sensorless motor drives. IEEE Transactions On Industrial Electronics, 69(10), 9860-9871. https://dx.doi.org/10.1109/TIE.2021.3139173
Project: NRF-NRFF12-2020-0003 
Journal: IEEE Transactions on Industrial Electronics 
Abstract: This paper designs parameter adaptation algorithms for online simultaneous identification of a two-parameter sigmoid inverter model for compensating inverter nonlinearity to reduce the voltage error in flux estimation for a position sensorless motor drive. The inverter model has two parameters, a2 and a3, where a2 is plateau voltage, and a3 is a shape parameter that mainly accounts for the stray capacitor effect. Parameter a3 is identified by the (6k 1)-th order harmonics in measured current. Parameter a2 is identified by the amplitude mismatch of the estimated active flux. It is found that the classic linear flux estimator, i.e., the hybrid of voltage model and current model, cannot be used for a2 identification. This paper proposes to use a saturation function based nonlinear flux estimator to build an effective indicator for a2 error. The coupled identifiability of the two parameters is revealed and analyzed, which was not seen in literature. The concept of the low current region where the two way coupling between a2 and a3 occurs is established. The experimental results in which dc bus voltage variation and load change are imposed, have shown the effectiveness of the proposed online inverter identification and compensation, even near low current region.
URI: https://hdl.handle.net/10356/157166
ISSN: 0278-0046
DOI: 10.1109/TIE.2021.3139173
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIE.2021.3139173.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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