Parameter Estimation for Deep-Bar Induction Machines Using Instantaneous Stator Measurements From a Direct Startup

Joseph Benzaquen, Johnny Rengifo, Eduardo Albánez, José M. Aller

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

A parameter estimation method for deep-bar induction machines is presented. The parameters are estimated using two instantaneous voltage and current waveforms during a direct startup. The instantaneous input impedance is used as a stator indicator to solve a constrained nonlinear optimization problem that outputs the model's parameters. For such purposes, a novel analytical expression for the instantaneous input impedance is introduced. The method is validated in two distinct National Electrical Manufacturers Association (NEMA) design type induction machines (designs A and B), and the accuracy of the obtained parameters is determined by comparing the instantaneous input impedance magnitude and angle errors between the deep-bar and single-cage models with experimental data. The two tested motors showed an improvement when implementing the deep-bar model with the estimated parameters. The error decrease is more significant for the NEMA design B motor which corresponds to a deep-bar rotor construction. Finally, the single-cage and deep-bar models are simulated and their outputs are compared to experimental waveforms. The deep-bar model with the estimated parameters outperforms the single-cage model, showing excellent agreement between the experimental and simulated mechanical speed, stator currents, and electromagnetic torque. The results endorse the accuracy of the method and its applicability for transient studies.

Original languageEnglish
Article number7831460
Pages (from-to)516-524
Number of pages9
JournalIEEE Transactions on Energy Conversion
Volume32
Issue number2
DOIs
StatePublished - Jun 2017

Bibliographical note

Funding Information:
Manuscript received May 1, 2016; revised September 8, 2016 and December 5, 2016; accepted January 7, 2017. Date of publication January 24, 2017; date of current version May 18, 2017. This work was supported by the FONACIT-Venezuela Research Projects #2011000970 and #201400195. Paper no. TEC-00380-2016.

Keywords

  • Deep-bar model
  • induction machine
  • parameter estimation
  • transient measurements

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