Torque and Flux Ripple Reduction of Direct Torque Control for Induction Motor Using Intelligent Technique
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Torque and Flux Ripple Reduction of Direct Torque Control for Induction Motor Using Intelligent Technique

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Abstract
The aim of this paper is reduction of torque and flux ripples in transient and steady state response of Direct Torque Control (DTC) for Induction Motor drive using intelligent technique. This proposed control to improve the torque, speed and flux response will be achieved with the Artificial Neural Network (ANN) DTC than the Conventional DTC (CDTC). In this paper DTC system using ANN is successfully implemented on three phase induction motor to optimize the flux and to improve the performance of fast stator flux response in transient state. To improve the performance of DTC with the modern technique using ANN approach is implemented, and performance of ANN DTC compared with CDTC is done, hence the ANN approach shows the better performance. The performance has been tested by using MATLAB/SIMULINK and NEURAL NETWORK toolbox.
Keywords:Direct Torque Control (DTC), Artificial Neural Network (ANN), and Conventional DTC (CDTC).
INTRODUCTION
The induction motor is work horse in all industrial applications due to its well known advantages of simple in construction, ruggedness and inexpensive and are available at all power ratings. In the field of power electronics enables the application of induction motors for high performance drives were traditionally replaced the DC motors were applied. The modern sophisticate control methods of induction motor drives offer the same control capabilities as high performance four quadrant DC drives. Induction motor drives controlled by Field Oriented Control (FOC) have been till now employed in high performance industrial applications, has achieved a quick torque response, and has been applied in various industrial applications instead of DC motors. It permits independent control of the torque and flux by decoupling the stator current into two orthogonal components FOC, however, is very sensitive to flux, which is mainly affected by parameter variations. It depends on accurate parameter identification to achieve the expected performance. During the last two decade a new control method called Direct Torque Control (DTC) has been developed for electrical machines. DTC principles were first introduced by Depenbrock and Takahashi. [1-3] In this method, stator voltage vectors is selected according to the differences between the reference and actual torque and stator flux linkage. The DTC method is characterized by its simple implementation and a fast dynamic response. Furthermore, the inverter is directly controlled by the algorithm, i.e., a modulation technique for the inverter is not needed. However if the control is implemented on a digital system, the actual values of flux and torque could cross their boundaries too far. The main advantages of DTC are absence of coordinate transformation and current regulator; absence of separate voltage modulation block. Common disadvantages of Conventional DTC are high torque ripple and slow transient response to the step changes in torque during start-up. For that reason the application of Intelligent Technique attracts the attention of many scientists from all over the world. The reason for this trend is the many advantages which the architectures of ANN have over traditional algorithmic of approximating non-linear functions, insensitivity to the distortion of the network, and inexact input data. In this paper we present the evaluation of flux and torque using the three stator currents is the voltage of input vector, and ANN has been devised having as inputs the torque error, the stator flux error and the position of the stator flux in which it lies, and as output the voltage vector to be generate by the inverter. The results are discussed and compared with CDTC.

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