A study on the speed estimation methods of induction motor drives in the field weakening region

2005 ◽  
Author(s):  
Suk-Kyum Kim ◽  
Myoung-Ho Shin ◽  
Dong-Seok Hyun
Keyword(s):  
Induction Motor ◽  
Motor Drives ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Induction Motor Drives ◽  
Field Weakening

scholarly journals Sensorless Induction Motor Drives Using Adaptive Flux Observer at Low Frequencies

2018 ◽  
Vol 8 (1) ◽  
pp. 2572-2576 ◽  
Author(s):  
M. S. Zaky ◽  
H. A. Maksoud ◽  
H. Z. Azazi
Keyword(s):  
Induction Motor ◽  
Motor Drives ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Induction Motor Drives ◽  
Machine Model ◽  
Low Frequencies ◽  
Flux Observer ◽  
Regenerating Mode

Operation at low frequencies of sensorless drives using machine model-based estimation methods is a challenging issue. This paper proposes an adaptive flux observer (AFO) method of speed estimation for sensorless induction motor drives. The observer feedback gains are designed to guarantee accurate speed estimation, especially at low frequencies in the regenerating mode operation. A complete sensorless IM drive with the proposed AFO is executed in the laboratory. Extensive experimental results under different operating conditions are provided to prove the effectiveness of the proposed AFO, particularly under low stator frequencies in both motoring and regenerating modes of operation.

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.

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