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http://dx.doi.org/10.5370/JEET.2014.9.2.550

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

Reza, C.M.F.S. (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya)
Islam, Didarul (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya)
Mekhilef, Saad (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, University of Malaya)
Publication Information
Journal of Electrical Engineering and Technology / v.9, no.2, 2014 , pp. 550-558 More about this Journal
Abstract
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.
Keywords
ANN; Stator resistance estimator; DTC; IM; Motor drive; Temperature;
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