Adaptive Fuzzy-Based IRFOC of Speed Sensorless Six-Phase Induction Motor Drive System

Abstract

In recent times, instead of three-phase induction motors (IM), multi-phase motors have been used in several applications, due to numerous advantages that they offer. Elimination of mechanical position or speed sensors allures for adjustable speed drives of IM. It leads to cost reduction, lesser maintenance and increased reliability of the motor drive. To eliminate the speed sensor, the rotor speed is estimated from measured stator currents and voltages at motor terminals. This paper proposes a scheme based on speed estimation method using Model Reference Adaptive System (MRAS) in conjunction with Adaptive Fuzzy Knowledge Based Controller (AFKBC) to improve the performance of a sensorless Indirect Rotor Field Oriented Control (IRFOC) of six-phase IM drive. AFKBC allows to operate at various operating conditions. A fitness function is defined for Queen Bee-based Genetic Algorithm (QBGA) to tune the scaling factor of AFKBC which improves the transient and steady state performances. The performance of the proposed scheme is evaluated by simulation on Matlab/Simulink package. The results of the proposed scheme are compared with conventional PI-based speed controller of the drive system. The proposed controller scheme improves the performance of the drive system at various operating conditions. The estimated speed algorithm gives good correlation between estimated and actual motor speed. The simulation results also illustrate that proposed scheme is robust and suitable for high performances six-phase IM drive.