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Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space.

Abstract:The purpose of this work is to present an adaptive sliding mode luenberger state observer with improved disturbance rejection capability and better tracking performance under dynamic conditions. The sliding hyperplane is altered by incorporating the estimated disturbance torque with the stator currents. Also, the effects of parameter detuning on the speed convergence is observed and compared with the conventional disturbance rejection mechanism. The entire drive system is first built in simulink environment. Then, the simulink model is integrated with RT-Lab blocksets and implemented in a relatively new real-time environment using OP4500 real-time simulator. Real-time simulation and testing platforms have succeeded offline simulation and testing tools due to their reduced development time. The real-time results validate the improvement in the proposed state observer and also correspond to the performance of the actual physical model.

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper analyses the effect of incorrect setting of parameters like the stator resistance, rotor time constant, load torque variations and also Voltage unbalance on various adaptive control based speed estimation techniques fed from the machine model. It also shows how the convergence mechanisms of the adaptation schemes are affected during these conditions. The equivalent models are built and simulated offline using MATLAB/SIMULINK blocksets and the results are analysed. Keyword:Adaptive Corresponding Author:Mohan Krishna.S, School of Electrical Engineering (SELECT), VIT University, Chennai Campus, Chennai -600 127, India. Email: smk87.genx@gmail.com INTRODUCTIONThe essence of employing encoderless induction motor drives is to eliminate additional space and cost which would otherwise be attributed to the speed encoder. The use of speed encoders also acts contrary to the inherent robustness of the induction motors. Therefore, estimation of speed without speed sensors emerged as an important concept [1]. Great amount of research has been done in this regard and it continues to inspire more, with the onset of artificial intelligence based speed estimation and other emerging technologies. The speed can be estimated either from the magnetic saliencies or by a machine model fed by terminal quantities. Owing to the complexity of speed estimation, the most discussed problems were the estimator's sensitivity to motor parameter changes, low and zero speed operation, speed estimation at field weakening region, stability problems in the regenerative mode etc. This paper attempts to present a performance analysis of various adaptive control schemes when they are subjected to load perturbations, incorrect parameter settings (Stator resistance and Rotor time constant) and Voltage unbalance. The effect of the same on the convergence of the adaptive mechanism is also presented.

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