Parameter Identification of Inverter-Fed Induction Motors: A Review

被引:34
|
作者
Tang, Jing [1 ]
Yang, Yongheng [2 ]
Blaabjerg, Frede [2 ]
Chen, Jie [1 ]
Diao, Lijun [1 ]
Liu, Zhigang [1 ,3 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect Engn, Beijing 100044, Peoples R China
[2] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark
[3] Beijing Engn Res Ctr Elect Rail Transit, Beijing 100044, Peoples R China
关键词
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; ROTOR TIME-CONSTANT; RESISTANCE ESTIMATION SCHEME; ARTIFICIAL NEURAL-NETWORKS; SLIDING MODE OBSERVER; TEMPERATURE ESTIMATION; AC DRIVES; GENETIC ALGORITHMS; SENSORLESS CONTROL; ADAPTIVE SCHEME; TORQUE CONTROL;
D O I
10.3390/en11092194
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:21
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