ANN based intelligent control of Induction Motor drive with Space Vector Modulated DTC

Jadhav, Sadhana V.; Kirankumar, J.; Chaudhari, B. N.

doi:10.1109/pedes.2012.6484350

Cited by 19 publications

(10 citation statements)

References 11 publications

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“…Several studies have suggested the application of the ANN technique to select the states of the voltage inverter switches used to power the DTC-controlled IM [107][108][109][110][111][112]. The idea is always to replace the conventional switching table that determine the inverter states by neural selector capable of managing control signals in the same way.…”

Section: Neural Network Based Dtcmentioning

confidence: 99%

Modern improvement techniques of direct torque control for induction motor drives - a review

Ouanjli¹,

Derouich²,

Ghzizal³

et al. 2019

Prot Control Mod Power Syst

105

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Conventional direct torque control (DTC) is one of the excellent control strategies available to control the torque of the induction machine (IM). However, the low switching frequency of the DTC causes high ripples in the flux and torque that leads to an acoustic noise which degrades the control performances, especially at low speeds. Many direct torque control techniques were appeared to remedy these problems by focusing specifically on the torque and flux. In this paper, a state of the art review of various modern techniques for improving the performance of DTC control is presented. The objective is to make a critical analysis of these methods in terms of ripples reduction, tracking speed, switching loss, algorithm complexity and parameter sensitivity. Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for academic and industrial researchers.

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Section: Neural Network Based Dtcmentioning

confidence: 99%

Modern improvement techniques of direct torque control for induction motor drives - a review

Ouanjli¹,

Derouich²,

Ghzizal³

et al. 2019

Prot Control Mod Power Syst

105

View full text Add to dashboard Cite

show abstract

“…To use fully real-time digital simulation a recent alternative way of testing that is fast becoming is quite popular. Interfacing of these simulations with industrial controllers, thus saving a lot of cost of the investment amount and an economic tool is allowed for testing of drive controller in all power ranges and offering the machine simulations flexible [2].…”

Section: Introductionmentioning

confidence: 99%

“…Where From the inductances of stator, rotor and mutual and leakage, the voltage equations are categorized. The fluxes and currents are articulated with real and imaginary components [2]. and According to is fixed to the real axis, consequently the .…”

Section: Introductionmentioning

confidence: 99%

Electrical Drive System Modeling for Real-Time Digital Simulation Applications

Srikanth¹,

Rao²

2019

IJRTE

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In this paper the digital simulation of physical system in MATLAB-SIMULINK for real-time applications is simulated for partial-scale or full-scale and validated simulation results with the existing system. One of the applications is AC drive systems with speed adjustability not only limited to equipment’s of electrical. The proper selection of AC motor drive is one of the main resolves of this paper. The efficient control of speed and torque is the second aim by considering the flux weakening regions.

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“…Rather, it can be combined with other techniques to enhance the performance of the drive as in [14] , where Field Oriented Control (FOC) and DTC [15] are combined to make a hybrid network and both the schemes FOC and DTC are mapped using two different ANNs. In [16], ANN is reported in control voltage vector loop. It can be seen that the ripple in torque with ANN-DTC control is very less as compared to conventional DTC at the same operating conditions [17].…”

Section: Introductionmentioning

confidence: 99%

Scaled Conjugate Gradient Based Adaptive ANN Control for SVM-DTC Induction Motor Drive

Babani

Jadhav

Chaudhari

2016

IFIP Advances in Information and Communication Technology

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In this work, an Artificial Neural Network (ANN) is developed to improve the performance of Space Vector Modulation (SVM) based Direct Torque Controlled (DTC) Induction Motor (IM) drive. The ANN control algorithm based on Scaled Conjugate Gradient (SCG) method is developed. The algorithm is tested on MATLAB Simulink platform. Results show smooth steady state operation as well as fast and dynamic transient performance. This is due to the SCG training algorithm of ANN which has the benchmarked performance against the standard Back-propagation (BP) algorithm. BP uses gradient descent optimization theory which has user selected parameters; learning rate and momentum constant. The network is trained offline and has fixed parameters. This leads to extra control effort and demands for online tuning of the parameters. SCG algorithm tunes these parameters with the use of second order approximation. Additionally, it takes less learning iterations and hence results in faster learning. Robustness to parameter variations and disturbances is the basic advantage of ANN, thus effectively controlling inherently non linear IM.

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ANN based intelligent control of Induction Motor drive with Space Vector Modulated DTC

Cited by 19 publications

References 11 publications

Modern improvement techniques of direct torque control for induction motor drives - a review

Modern improvement techniques of direct torque control for induction motor drives - a review

Electrical Drive System Modeling for Real-Time Digital Simulation Applications

Scaled Conjugate Gradient Based Adaptive ANN Control for SVM-DTC Induction Motor Drive

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