Parameter Identification of Inverter-Fed Induction Motors: A Review

Jing Tang, Yongheng Yang, Frede Blaabjerg, Jie Chen, Lijun Diao and Zhigang Liu
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Jing Tang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Yongheng Yang: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Frede Blaabjerg: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Jie Chen: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Lijun Diao: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Zhigang Liu: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

Energies, 2018, vol. 11, issue 9, 1-21

Abstract: Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Keywords: induction motor; parameter identification; offline parameter identification; online parameter identification; recursive least square; model reference adaptive system; signal injection; extend Luenberger observer; sliding mode observer; extend Kalman observer; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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