Stator resistance tuning in a stator-flux field-oriented drive using an instantaneous hybrid flux estimator

Habetler, T.G.; Profumo, F.; Griva, G.; Pastorelli, M.; Bettini, A.

doi:10.1109/63.654966

Cited by 102 publications

(50 citation statements)

References 7 publications

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“…To achieve the control objectives, choosing the torque and the magnitude of the cross product of ∇T e (x 1 , x 2 ) and ∇(I 2 s , P in or S 2 in ) as output variables, output vector [y 1 y 2 ] T can be defined as (17) MTPA strategy (18) Efficiency-optimized control strategy (19) Min. input KVA strategy (20) Obviously chosen control strategy would be carried out when y 2 can be maintained in zero value.…”

Section: Selection Of Outputsmentioning

confidence: 99%

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Direct Torque Control of Synchronous Reluctance Motor Using Feedback Linearization Including Saturation and Iron Losses

2009

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Section: Selection Of Outputsmentioning

confidence: 99%

“…IOFL technique also, requires the full knowledge of the motor parameters with sufficient accuracy. Several control schemes have been proposed to overcome this issue [18,19]. These methods suffer from shortcomings such as restriction of the control range, problem of convergence and a lack of speed of response.…”

Section: Introductionmentioning

confidence: 99%

Direct Torque Control of Synchronous Reluctance Motor Using Feedback Linearization Including Saturation and Iron Losses

2009

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“…To estimate stator resistance, online identification has been developed using model reference adaptation [14]. Combined stator and rotor resistance identification has been reported [7,15]. However, these methods are based on the assumption that the stator resistance does not change during the estimation of rotor resistance.…”

Section: Introductionmentioning

confidence: 99%

Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks

Karanayil

Rahman

Grantham

2009

Advances in Fuzzy Systems

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This paper presents a new method of online estimation of the stator and rotor resistance of the induction motor in the indirect vector-controlled drive, with artificial neural networks. The back propagation algorithm is used for training of the neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, is investigated with the help of simulations for variations in the stator and rotor resistance from their nominal values. Both types of resistance are estimated experimentally, using the proposed neural network in a vector-controlled induction motor drive. Data on tracking performances of these estimators are presented. With this approach, the rotor resistance estimation was found to be insensitive to the stator resistance variations both in simulation and experiment.

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“…In this strategy the stator resistance calculation has been carried out in reference frame which aligned with stator current vector. In [11] stator voltage model has been used to calculate stator resistance. Stator resistance is evaluated from active power balance of machine in [12].…”

Section: Introductionmentioning

confidence: 99%

“…All schemes which uses steady state induction motor model and some measured quantities to calculate explicitly the stator resistance [9][10][11][12][13] can be categorized as the first group. In [9] reactive power is evaluated using measured terminal variables, rotor and stator flux is evaluated next and then drive torque is evaluated.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Experimental Verification of ANN Based Online Stator Resistance Estimation in DTC-IM Drive

Reza¹,

Islam²,

Mekhilef³

2014

Journal of Electrical Engineering and Technology

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-Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

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Stator resistance tuning in a stator-flux field-oriented drive using an instantaneous hybrid flux estimator

Cited by 102 publications

References 7 publications

Direct Torque Control of Synchronous Reluctance Motor Using Feedback Linearization Including Saturation and Iron Losses

Direct Torque Control of Synchronous Reluctance Motor Using Feedback Linearization Including Saturation and Iron Losses

Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks

Modeling and Experimental Verification of ANN Based Online Stator Resistance Estimation in DTC-IM Drive

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