Reactive-power-based MRAC for rotor resistance and speed estimation in bearingless induction motor drives

2020 ◽  
Vol 62 (1) ◽  
pp. 127-143
Author(s):  
Ke Li ◽  
Feng Ling ◽  
Xiaodong Sun ◽  
Dean Zhao ◽  
Zebin Yang
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Motor Drives ◽  
Speed Estimation ◽  
Induction Motor Drives ◽  
Rotor Resistance

Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives

2013 ◽  
Vol 62 (1) ◽  
pp. 25-41 ◽  
Author(s):  
K. Sedhuraman ◽  
S. Himavathi ◽  
A. Muthuramalingam
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Adaptive System ◽  
Learning Algorithm ◽  
Motor Drives ◽  
Speed Estimation ◽  
Induction Motor Drives ◽  
Model Reference ◽  
Neural Learning ◽  
Speed Estimator

Abstract This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

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

2012 ◽  
Vol 9 (2) ◽  
pp. 17 ◽  
Author(s):  
K Sedhuraman ◽  
S Himavathi ◽  
A Muthuramalingam
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Reactive Power ◽  
Adaptive System ◽  
Learning Algorithms ◽  
Motor Drives ◽  
Speed Estimation ◽  
Induction Motor Drives ◽  
Model Reference ◽  
Neural Learning

In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRNLAS) 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 RPMRNLAS. 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 

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