TY - JOUR
T1 - Parameter Identification of Inverter-Fed Induction Motors
T2 - A Review
AU - Tang, Jing
AU - Yang, Yongheng
AU - Blaabjerg, Frede
AU - Chen, Jie
AU - Diao, Lijun
AU - Liu, Zhigang
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
KW - induction motor
KW - parameter identification
KW - offline parameter identification
KW - online parameter identification
KW - recursive least square
KW - model reference adaptive system
KW - signal injection
KW - extend Luenberger observer
KW - sliding mode observer
KW - extend Kalman observer
KW - artificial intelligence
UR - http://www.scopus.com/inward/record.url?scp=85054030067&partnerID=8YFLogxK
U2 - 10.3390/en11092194
DO - 10.3390/en11092194
M3 - Review article
SN - 1996-1073
VL - 11
SP - 1
EP - 21
JO - Energies
JF - Energies
IS - 9
M1 - 2194
ER -