Particle Swarm Optimized Direct Torque Control of Induction Motors

• Published in: IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics pp. 1586 - 1591
• Main Authors: El-Laban, O.S., Fattah, H.A.A., Emara, H.M., Sakr, A.F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2006
• Subjects: Adaptive systems; Artificial neural networks; Fuzzy logic; Hysteresis motors; Induction motors; Particle swarm optimization; Power system modeling; Stators; Torque control; Voltage control
• DOI: 10.1109/IECON.2006.347342
• ISSN: 1553-572X

The flux and torque hysteresis bands are the only adjustable parameters in direct torque control (DTC) of induction motors. Their selection greatly influences the inverter switching loss, motor harmonic loss and motor torque ripples, which are major performance criteria. In this paper, the effects of flux and torque hysteresis bands on these criteria are investigated and optimized via the minimization, by the particle swarms optimization (PSO) technique, of a suitably selected cost function. A DTC control strategy with variable hysteresis bands, which improves the drive performance compared to the classical DTC, is proposed. Online operating artificial neural networks (ANNs) use the offline optimum values obtained by PSO, to modify the hysteresis bands in order to improve the performance. The implementation of the proposed scheme is illustrated by simulation results