Rotor flux and torque estimator for vector controlled induction drive using ANN

Neema, D. D.; Patel, R. N.; Thoke, A. S.

doi:10.1109/ijcnn.2009.5178881

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…An error of estimation would be high if the number of neurons is low. Conversely, the network would tend to memorize rather than learn [17]. In order to determine the optimum ANN structure, some off-line training results are effective in both of them.…”

Section: Methodsmentioning

confidence: 99%

Reduction of torque ripple in induction motor by artificial neural multinetworks

Korkmaz¹,

Topaloğlu²,

Mamur³

et al. 2016

Turk J Elec Eng & Comp Sci

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Direct torque control is used in the high performance control of induction motors. The most frequently faced problem of it is high torque ripples. In this study, a new approach based on artificial neural multinetworks is presented to overcome the problem. Two different artificial neural networks were suggested instead of vector selection and sector determination processes in the conventional direct torque control method. The conventional and the proposed control methods were evaluated on an induction motor through an experimental set. It was observed that the speed and torque responses of the proposed method were better than those of the conventional method. The experimental results also show that the proposed method would be a good alternative to the conventional method in induction motors.

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Section: Methodsmentioning

confidence: 99%

Reduction of torque ripple in induction motor by artificial neural multinetworks

Korkmaz¹,

Topaloğlu²,

Mamur³

et al. 2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

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“…The artificial neural network provides a nonlinear modeling of motor drive system [24] without any knowledge of predetermined model and thus makes the drive system robust to noise, parameter variations, load changes. An adaptive neuro-fuzzy inference system (ANFIS) based intelligent control [25][26] for vector controlled induction motor drive incorporates fuzzy logic algorithm with a five-layer artificial neural network structure [27]. A vector control structure by combining the advantages of two types of field oriented procedure has been discussed for the squirrel cage induction motor fed from a PWM based voltage source inverter (VSI).…”

Section: Introductionmentioning

confidence: 99%

Speed regulation of IVCIM using PI, fuzzy and NN predictive controller

Bhushar

Singh

Valluru

et al. 2012

2012 IEEE Fifth Power India Conference

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In this paper, performance of an indirect vector control induction motor (IVCIM) has been studied with proportional plus integral (PI), fuzzy logic and neural network (NN) based controllers. Implementation of PI, fuzzy logic control and NN predictive control for studies of regulation of speed in IVCIM is described under different operating conditions of induction motor. Simulation studies of IVCIM have been carried out in MATLAB. Stator currents, electromagnetic torque and rotor speed of IVCIM have been analyzed using three controllers. A comparative performance analysis of IVCIM with three different controllers has been discussed.

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