oa Sensorless Direct Power Control Of Induction Motor Drive Using Artificial Neural Network

This paper proposes design of sensorless induction motor drive based on direct power control (DPC) technique. Principle of DPC technique is presented and possibilities of direct power control (DPC) for induction motors (IMs) fed by a space vector pulse-width modulation inverter (SV-PWM) are studied. The simulation results show that the DPC technique enjoys all advantages of pervious method such as fast dynamic and ease of implementation, without having the previous method problems. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify proposed methods. Results show that DPC of IMs works well with output power and flux control. Moreover, to reduce cost of drive and enhancing reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor's position and motor speed. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control of induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been designed and trained online by employing a back propagation network (BPN) algorithm. The estimator is simulated in Matlab/Simulink. Simulation results confirm the performance of ANN-based sensorless DPC induction motor drive in various conditions.