Sensorless Control of Induction motor using model reference Adaptive System

Abstract

Sensor less control finds a lot of attention due to its low cost, high reliability and mechanical robustness which is due to non-presence of mechanical sensors and low mechanical hardware requirements. Due newlineto the absence of mechanical sensors in the sensorless control, the speed of the machine is to be estimated by using estimation newlinetechniques. Of the different estimation techniques, Model Reference Adaptive System (MRAS) technique finds a lot of attention due to its simplicity and robustness. In this method there are two models namely reference model and adjustable model. The speeds of these two models are compared and the error hence obtained is minimized by using an adaptation mechanism usually consisting of a PI controller. As the speed error in MRAS scheme is minimized, this method is found to be more robust than any other estimation method. Even though the MRAS scheme is found to be simple and robust it has few drawbacks such as difficulty in speed estimation during low and very low speed regions, presence of ripples in torque and stator currents and speed of the motor getting affected by external disturbances such as load variations. In order to minimize the difficulty in speed estimation during low speed region a two stage high pass filter (HPF) with low cut off frequency is proposed in this thesis, which is cascaded to the reference model removing initial ondition and DC drift problems. With the introduction of HPF a time delay in the build up of stator flux. newlineviii newlineis caused which makes the stator flux to decrease rapidly at starting newlinewhen the drive is switched from standstill to sensorless vector control newlinemode which generates a jerk in the motor torque at starting. The newlinecompensation for rapid decrease in stator flux is achieved by newlinefeedforward control of stator flux, hence preventing the generation of newlinetorque jerk at starting. newline