Normalized-Model Reference System for Parameter Estimation of Induction Motors

Adolfo Véliz-Tejo, Juan Carlos Travieso-Torres, Andrés A. Peters, Andrés Mora and Felipe Leiva-Silva
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Adolfo Véliz-Tejo: Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago 8940572, Chile
Juan Carlos Travieso-Torres: Department of Industrial Technologies, University of Santiago de Chile, Santiago 9170125, Chile
Andrés A. Peters: Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Santiago 7941169, Chile
Andrés Mora: Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago 8940572, Chile
Felipe Leiva-Silva: Department of Industrial Technologies, University of Santiago de Chile, Santiago 9170125, Chile

Energies, 2022, vol. 15, issue 13, 1-29

Abstract: This manuscript proposes a short tuning march algorithm to estimate induction motors (IM) electrical and mechanical parameters. It has two main novel proposals. First, it starts by presenting a normalized-model reference adaptive system (N-MRAS) that extends a recently proposed normalized model reference adaptive controller for parameter estimation of higher-order nonlinear systems, adding filtering. Second, it proposes persistent exciting (PE) rules for the input amplitude. This N-MRAS normalizes the information vector and identification adaptive law gains for a more straightforward tuning method, avoiding trial and error. Later, two N-MRAS designs consider estimating IM electrical and mechanical parameters. Finally, the proposed algorithm considers starting with a V/f speed control strategy, applying a persistently exciting voltage and frequency, and applying the two designed N-MRAS. Test bench experiments validate the efficacy of the proposed algorithm for a 10 HP IM.

Keywords: parameter estimation; adaptive systems; induction motors; nonlinear dynamical systems; persistent excitation (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: 2022
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