A new adaptive approach for on-line parameter and state estimation of induction motors

Castaldi, Paolo; Geri, Walter; Montanari, Marcello; Tilli, Andrea

doi:10.1016/j.conengprac.2004.02.008

Cited by 50 publications

(22 citation statements)

References 20 publications

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“…5 without any speed measurement. The control is realized when the voltage and current of one phase of stator are tracked without any geometric transformation, whereas in the case of the vector control based on the DQ model, it is necessary to track two voltages (v as and v bs ) and two currents (i as and i bs ) when applying Park transformation (Barut, Bongosyan, & Gokasan, 2002;Castaldi, Geri, Montanari, & Tilli, 2005;Loron and Laliberte, 1993;Mueller, 1999;Po¨yho¨nen, Arkiko, Jover, & Hyo¨tyniemi, 2005;Vas, 1990;Yazid, Menaa, Touhami, & Ibtiouen, 2005).…”

Section: Simulation Resultsmentioning

confidence: 99%

Sensorless direct vector control of an induction motor

Menaa¹,

Touhami²,

Ibtiouen³

et al. 2008

Control Engineering Practice

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Section: Simulation Resultsmentioning

confidence: 99%

Sensorless direct vector control of an induction motor

Menaa¹,

Touhami²,

Ibtiouen³

et al. 2008

Control Engineering Practice

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“…Taking these considerations into account, a mathematical model of the IM can be obtained [1], [13], which can be expressed as follows:…”

Section: Mathematical Model Of the Induction Motormentioning

confidence: 99%

An Optimisation-Based Approach for Designing Indirect Sensors Applied to an Induction Motor

Valdés-González

Valdés-González²,

Zolezzi

2006

Rev. Fac. Ing. - Univ. Tarapacá

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Valdés et al.:An optimisation-based approach for designing indirect sensors applied to an induction motorRev. Fac RESUMENEste artículo propone una novedosa estrategia aplicada al problema de estimación no lineal de estados en un motor de inducción (MI). Este método permite, en general, la estimación de variables de difícil acceso o que simplemente no se pueden medir. Lo cual es posible a través de mediciones indirectas, considerando un modelo dinámico del proceso y un algoritmo de estimación basado en optimización no lineal. El principal atractivo de esta estrategia de estimación de estados denominada MHSE (Moving Horizon State Estimation) en un MI, que permite conocer la magnitud del flujo, la velocidad o posición del rotor, es su simplicidad de implementación, sus buenas características de convergencia, su independencia de estructuras preestablecidas de modelos y su fácil sintonía. Resultados en simulación muestran la efectividad del método propuesto efectuando la estimación de la velocidad de un MI bajo diferentes puntos de operación.Palabras clave: MHSE, estimación de estados, motor de inducción, optimización no lineal, horizonte móvil. ABSTRACT This article proposes an innovative strategy to the problem of non-linear estimation of states in an induction motor, (IM

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“…The components of the parameter vector are related as (13) showing that only the two parameters are independent. These two parameters determine and by (14) Though the system regressor is linear in the parameters, it is overparameterized, resulting in poor numerical conditioning if standard least-squares techniques are used.…”

Section: Induction Motor Modelmentioning

confidence: 99%

“…That is, small changes in the data , lead to large changes in the value computed for . To get around this problem, a nonlinear least-squares approach is taken which involves minimizing (18) subject to the constraints (13), where On physical grounds, the parameters are constrained to the region (19) Also, based on physical grounds, the squared error will be minimized in the interior of this region. Define the new error function as…”

Section: Least-squares Identification [19]-[21]mentioning

confidence: 99%

An online rotor time constant estimator for induction machine

Wang

Chiasson

Bodson

et al. 2005

IEEE International Conference on Electric Machines and Drives, 2005.

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Abstract-Indirect field-oriented control of an induction machine requires knowledge of the rotor time constant to estimate the rotor flux linkages. Here, an online method is presented for estimating the rotor time constant and the stator resistance, both of which vary during operation of the machine due to ohmic heating. The method uses measurements of the stator voltages, stator currents, and their derivatives (first derivative of the voltages and both the first and second derivatives of the currents). The problem is formulated as finding those parameter values that best fit (in a least-squares sense) the model of the induction motor to the measured output data of the motor. This method guarantees that the parameter values are found in a finite number of steps. Experimental results of an online implementation are presented.

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A new adaptive approach for on-line parameter and state estimation of induction motors

Cited by 50 publications

References 20 publications

Sensorless direct vector control of an induction motor

Sensorless direct vector control of an induction motor

An Optimisation-Based Approach for Designing Indirect Sensors Applied to an Induction Motor

An online rotor time constant estimator for induction machine

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