Online identification and adaptation of rotor resistance in feedforward vector controlled induction motor drive

Chandra, B. Mouli; Kalyani, S. Tara

doi:10.1109/iicpe.2012.6450372

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Therefore, in order to ensure full motor performances, the rotor resistance has to be estimated continuously. Different observers, such as full-order state observers [10,11], extended Kalman filters [12][13][14], Model Reference Adaptive System (MRAS)-based estimators [15][16][17][18][19][20][21] and neural networks [22,23] are proposed in literature. The main drawback of many of these algorithms is that they require a high performance hardware to compute the estimated values, since they need to solve complex non-linear equations.…”

Section: Introductionmentioning

confidence: 99%

MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results

Mapelli

Tarsitano

Cheli

2017

Electric Power Systems Research

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Section: Introductionmentioning

confidence: 99%

MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results

Mapelli

Tarsitano

Cheli

2017

Electric Power Systems Research

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“…Therefore, in order to ensure full motor performances, the rotor resistance has to be estimated continuously. Different observers, such as fullorder state observers [9]- [10], extended Kalman filters [11]- [13], Model Reference Adaptive System (MRAS)-based estimators [16]- [20] and neural networks [21]- [22] are proposed in literature. The main drawback of many of these algorithms is that they require a high performance hardware to compute the estimated values, since they need to solve complex non-linear equations.…”

mentioning

confidence: 99%

A rotor resistance MRAS estimator for EV induction motor traction drive based on torque and reactive stator power: Simulation and experimental results

Mapelli

Tarsitano

Cheli

2014

2014 International Conference on Electrical Machines (ICEM)

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The Field Oriented Controlled algorithm needs accurate estimation of motor state variables in order to ensure full torque and power performance. Good control results are strongly related to parameter values used by observers or estimators' algorithms, which vary according to the machine working conditions and the temperature. The most important parameter is the rotor resistance. The paper shows and compares two different MRAS rotor resistance estimators, based on reactive power and motor torque, studied by means of a sensitivity analysis for different load and speed operating conditions. A nonlinear correction algorithm has been proposed in order to assure a good rotor resistance estimation convergence. Since the algorithm has to be implemented on an electrical vehicle inverter, it has been defined taking into account that it has to operate under dynamic conditions, the typical situation occurring during a drive cycle. Sensitivity analysis, simulation and experimental results are reported for the proposed methods.Index Terms--Adaptive algorithm, full electric vehicle, online rotor resistance estimation, MRAS approach, induction motor, field oriented control, sensitivity analysis. I. NOMENCLATUREs v Stator voltage s i Stator current s ψ Stator flux r ψ Rotor flux s R Stator resistance r R Rotor resistance r R ∆ Error between estimated and real rotor resistance M Mutual inductance ks L Total leakage inductance n Pole-pairs number T Torque Q Motor reactive power x e Error between real and estimated value of the generic variable x s θ ɺ Reference frame angular speed (synchronism speed) θ ɺ Mechanical angular speed (at magnetic field) m Ω Mechanical angular speed (at shaft, / m n θ Ω = ɺ ) r θ ɺ Absolute rotor slip speed ( r s θ θ θ = − ɺ ɺ ɺ ) F. L. Mapelli is with the γ Angle between the stator current and the rotor flux space vectors ( ) h ℑ Imaginary part of a generic complex number h j Imaginary unit p Derivative symbol II. INTRODUCTION NDUCTION motors are widely used in railway [1] and in Electrical Vehicles (EV) [2]-[4] traction applications due to their simplicity, robustness, reliability and low cost. The IM-based electrical drives are usually controlled according the Field Oriented Control (FOC) technique [5]-[7], which allows to control the speed and torque by means of flux and current regulation. In Fig. 1 the overall block diagram of the rotor flux sensored FOC scheme for an IM is presented. This classic control scheme has been chosen for its robustness and for its capability to work in full-field conditions. Since some of the quantities reported in the control scheme are not directly measurable, observers or estimators are needed. In order to work properly, these algorithms require the value of some motor parameters, which often cannot be exactly identified and may vary during the operation, especially because of temperature fluctuations. The most critical parameter is the rotor resistance, because the effectiveness of the control is strictly related to it, as well explained in [8]. Therefore, in order t...

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A Review on Parameter Estimation Techniques in Decoupling Control of Induction Motor Drive

Chandra¹,

Prasantlr²,

Manindher³

2018

2018 4th International Conference on Electrical Energy Systems (ICEES)

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Online identification and adaptation of rotor resistance in feedforward vector controlled induction motor drive

Cited by 3 publications

References 22 publications

MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results

MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results

A rotor resistance MRAS estimator for EV induction motor traction drive based on torque and reactive stator power: Simulation and experimental results

A Review on Parameter Estimation Techniques in Decoupling Control of Induction Motor Drive

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