Abstract
This paper proposes the design of sensorless induction motor drive based on direct power control (DPC) technique. It is shown that DPC technique enjoys all advantages of pervious methods such as fast dynamic and ease of implementation, without having their problems. To reduce the cost of drive and enhance the reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor’s position and speed of induction motor. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Simulink. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify the proposed methods. Simulation results confirm the performance of ANN based sensorless DPC induction motor drive in various conditions.
Highlights
The electrical drive system is used to control the position, speed, and torque of the electric motors
Speed Estimation Using Neural Network In model reference adaptive system (MRAS) technique, some state variables, Xd and Xq, of the induction machine are estimated in a reference model and are compared with state variables Xd and Xq estimated by using an adaptive model
This paper studies the possibility of direct power control for speed control of induction motor
Summary
The electrical drive system is used to control the position, speed, and torque of the electric motors. The other two methods are in the space vector control category because they utilize both magnitude and angular position of space vectors of motor variables, such as the voltage and flux They are employed in high performance applications, such as positioning drives or electric vehicles [3, 4]. Speed estimator employing artificial neural network (ANN) is an improvement over the classical mathematical model based approaches [18,19,20,21,22,23] It is a major advantage of ANN based techniques that they do not require any mathematical model of the motor under consideration and the drive development time can be substantially reduced.
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