Parameter Identification of Inverter-Fed Induction Motors: A Review

Jing Tang, Yongheng Yang, Frede Blaabjerg, Jie Chen, Lijun Diao, Zhigang Liu

Research output: Contribution to journalReview articlepeer-review

48 Citations (Scopus)
451 Downloads (Pure)

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.
Original languageEnglish
Article number2194
JournalEnergies
Volume11
Issue number9
Pages (from-to)1-21
Number of pages21
ISSN1996-1073
DOIs
Publication statusPublished - Aug 2018

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

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