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Research Papers

On-Line Parameter Identification of an Induction Motor With Closed-Loop Speed Control Using the Least Square Method

[+] Author and Article Information
Rafael de Farias Campos

Department of Electrical Engineering,
Santa Catarina State University,
Joinville SC 89219-710, Brazil
e-mail: rafacfar@gmail.com

Eduardo Couto

Whirlpool Latin America,
Joinville SC 89219-900, Brazil
e-mail: ehcouto@hotmail.com

Jose de Oliveira

Department of Electrical Engineering,
Santa Catarina State University,
Joinville SC 89219-710, Brazil
e-mail: jose.oliveira@udesc.br

Ademir Nied

Department of Electrical Engineering,
Santa Catarina State University,
Joinville SC 89219-710, Brazil
e-mail: ademir.nied@udesc.br

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 17, 2016; final manuscript received January 19, 2017; published online May 12, 2017. Assoc. Editor: Evangelos Papadopoulos.

J. Dyn. Sys., Meas., Control 139(7), 071010 (May 12, 2017) (8 pages) Paper No: DS-16-1253; doi: 10.1115/1.4035872 History: Received May 17, 2016; Revised January 19, 2017

The control system of induction motors is designed to achieve dynamic stability, allowing accurate tracking of flux and speed. However, changes in electrical parameters, due to temperature rise or saturation level, can lead to undesirable errors of speed and position, eventually resulting in instability. This paper presents two modes for parametric identification of the induction motor based on the least squares method: batch estimator and recursive estimator. The objective is to update the electrical parameters during operation when the motor is driven by a vector control system. A drawback related to the batch estimator is the need for high quantity of available memory to make the process of identification robust enough. The proposed algorithm allows the batch estimator to be viewed as a single matrix problem reducing the need for processing memory. The identification procedure is based on the stator currents measurement and stator fluxes estimation. Basically, both modes of identification will be analyzed. Experimental results are presented to demonstrate the theoretical approach.

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Figures

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Fig. 1

Spacial representation of the vector control principle: dq is the rotational reference frame; αβ is the stationary reference frame

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Fig. 2

Current control loops and the decoupling distortions

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Fig. 3

Recursive estimation procedure

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Fig. 4

System configuration for parametric estimation

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Fig. 5

Stator resistance estimation: Rs = (1.0Ω/V-CH1:1V/div)

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Fig. 6

Rotor flux linkage and rotor speed: ϕsdrf = (0.1Wb/V-CH1:2V/div), ϕsqrf = (0.1Wb/V-CH2:5V/div), ωr  = (180rpm/V-CH3:2V/div) e ωref  = (180rpm/V-CH4:2V/div)

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Fig. 7

Variables used in the parameter identification of the motor: stator flux linkage: ψsds = (0.1Wb/V-CH1:5V/div), ψsqs = (0.1Wb/V-CH2:5V/div). Stator current: Isds = (0.7A/V-CH3:2V/div), isqs = (0.7A/V-CH4:2V/div).

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Fig. 8

Discrete coefficients: discrete pole (f̂−CH1:5V/div). Discrete zero1 (b0̂−CH2:2V/div). Discrete zero2 (b1̂−CH3:2V/div).

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Fig. 9

Continuous coefficients: continuous pole (â−CH1:5V/div). Continuous zero1 (d0̂−CH2:2V/div). Continuous zero2 (d1̂−CH3:2V/div).

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Fig. 10

Estimation of the parameters of the motor using thebatch approach in closed-loop drive: Ls = (0.10H/V-CH1:2V/div), Lr = (0.10H/V-CH2:2V/div); Lsr = (0.10H/V-CH3:2V/div), Rr = (1.0Ω/V-CH4:1V/div)

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Fig. 11

Estimation of the discrete coefficients of the model of the motor obtained by the RLS approach in closed-loop drive: discrete pole (f̂−CH1:5V/div), (b0̂−CH2:2V/div), (b1̂−CH3:2V/div)

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Fig. 12

Estimation of the parameters of the motor obtained by the RLS approach in closed-loop drive: Ls = (0.10H/V-CH1:2V/div), Lr = (0.10H/V-CH2:2V/div); Lsr = (0.10H/V-CH3:2V/div), Rr = (1.0Ω/V-CH4:1V/div)

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