17-12-2012, 04:09 PM
Direct Torque Control of Induction Motor Using ANN Technique
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Abstract
In this paper, The Artificial Neural Network technique used for flux position estimation & sector selection to reduce the torque & flux ripples. Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. The DTC has some drawbacks, such as the high torque & flux ripples, caused by sector changes. The important point in ANN based DTC is the right selection of the stator voltage vector. This project presents simple structured neural networks for flux position estimation and sector selection for induction motors. The Levenberg-Marquardt back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The induction motor is non-linear system, the ANNs are excellent estimators in non-linear systems.
INTRODUCTION
major revolutions in the area of induction motor control was invention of field- oriented control (FOC) or vector control (by Blaschke and Hasse) 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. In vector control method it is necessary to determine correctly the orientation of the rotor flux vector and main draw back in field oriented control is rotor time constant of standard squirrel-cage induction machine is very large, thus rotor flux linkage changes slowly compared to the stator flux linkage. The new technique was developed for the induction motor torque control i.e Direct torque control (DTC). Direct torque control (DTC) of Induction machines presents a good tracking for both electromagnetic torque and stator flux. This control scheme, as shown in Fig-1, depends only stator measurements.
MODELING OF INDUCTION MACHINE
The induction motors advantages over the rest of the motors. The main advantage is that induction motors do not require an electrical connection between stationary and rotating parts of the motor. Therefore, they do not need any mechanical commutator (brushes), leading to the fact that they are maintenance free motors. Induction motors also have low weight and inertia, high efficiency and a high overload capability. Therefore, they are cheaper and more robust, and less proves to any failure at high speeds.
SPEED CONTROL TECHNIQUES
In the past, DC motors were used extensively in areas where variable-speed operations were required. DC motors have certain disadvantages, however, which are due to the existence of the commutator and the brushes which makes the motor more bulky, costly and heavy. They are also robust and immune to heavy loading. The speed of the induction motor has to be controlled and so different types of controllers are used to obtain the desired speed. Various speed control techniques implemented by modern-age Variable Frequency Drive are mainly classified in the following three categories
1. Scalar Control (V/f Control),
2. Vector Control (Indirect Torque Control)
3. Direct Torque Control (DTC).
The aim of our paper is to control the Speed & Torque of the induction motor using Direct torque control technique .
The dynamic modeling of induction motor is done in the SIMULINK using the necessary equations. The Direct torque control of the induction motor is also modeled in the SIMULINK using the necessary equations. Neural Networks is implemented in the system for better control of the induction motor.
ARTIFICIAL NEURAL NETWORKS
Numerous advances have been made in developing intelligent systems, some inspired by biological neural networks. Researchers from many scientific disciplines are designing artificial neural networks to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control. Conventional approaches have been proposed for solving these problems. Although successful applications can be found in certain well-constrained environments, none is flexible enough to perform well outside its domain. ANNs provide exciting alternatives, and many applications could benefit from using them. This article is for those readers with little or no knowledge of ANNs to help them understand the other articles in this issue of Computer.
CONCLUSION
From the above the conventional direct torque control technique is used for DTC control of Induction motor. In conventional DTC method some disadvantages such as difficulties in torque and flux control at very low speed, high current and torque ripple, variable switching frequency behavior, high noise level at low speed and lack of direct current control, an adaptive torque controller must be proposed for high performance applications. So an Artificial Neural Network (ANN) control is proposed for conventional DTC scheme. The intelligent technique ANN are used for proper sector selection in DTC so that the rotor speed, torque and flux performances of induction machine is improved. The conventional DTC controller compared with ANN. The results are carried out by using Mat-Lab/simulink