Neural computation based vector controlled asynchronous motor fed by three levels NPC

This study presents an improved direct torque control based on artificial neural network techniques fed by a three levels neutral-point-clamped inverter for high power asynchronous motor drive. Indeed, the ANN is divided into four sub-networks, which are individually trained: flux estimation (supervised) with dynamic neurons, torque calculation (fixed-weight) with square neurons, flux angle encoder and magnitude calculation (supervised and fixed-weight) with “logsig” neurons and “tansig” neurons. The back-propagation learning rule is used to design the supervised neural network. The simple structure network facilitates a short training and processing times. The validity of the proposed approaches is confirmed by the simulation.