Reactive Power based Model Reference Neural Learning Adaptive System for Speed Estimation in Sensor-less Induction Motor Drives

Abstract

In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRN-LAS) is proposed. The model reference adaptive system (MRAS) based speed estimation is one of the most popular methods used for sensor-less controlled induction motor drives. In conventional MRAS, the error adaptation is done using a Proportional-integral-(PI). The non-linear mapping capability of a neural network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. Thus, a neural learning algorithm is used for the adaptation mechanism in MRAS and is often referred to as a model reference neural learning adaptive system (MRNLAS). In MRNLAS, the error between the reference and neural learning adaptive models is back propagated to adjust the weights of the neural network for rotor speed estimation. The two different methods of MRNLAS are flux based (RF-MRNLAS) and reactive power based (RP-MRNLAS). The reactive power-based methods are simple and free from integral equations as compared to flux based methods. The advantage of the reactive power based method and the NN learning algorithms are exploited in this work to yield a RP-MRNLAS. The performance of the proposed RP-MRNLAS is analyzed extensively. The proposed RP-MRNLAS is compared in terms of accuracy and integrator drift problems with popular rotor flux-based MRNLAS for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated

Improved direct torque control of induction motors using adaptive observer and sliding mode control

“…Most methods are essentially based on the Model Reference Adaptive System (MRAS) or on the reactive power based reference model [4][5]. The MRAS algorithm is very simple but its greatest drawback is the sensitivity to uncertainties in the motor parameters.…”