Stator Fault Diagnostics in Squirrel Cage Three-Phase Induction Motor Drives Using the Instantaneous Active and Reactive Power Signature Analyses

2014 ◽  
Vol 10 (2) ◽  
pp. 1348-1360 ◽  
Author(s):  
M'hamed Drif ◽  
Antonio J. Marques Cardoso
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Motor Drives ◽  
Fault Diagnostics ◽  
Induction Motor Drives ◽  
Squirrel Cage ◽  
Active And Reactive Power ◽  
Three Phase ◽  
Stator Fault

scholarly journals Direct Torque Control for Dual Three-Phase Induction Motor Drives

2005 ◽  
Vol 41 (6) ◽  
pp. 1627-1636 ◽  
Author(s):  
R. Bojoi ◽  
F. Farina ◽  
G. Griva ◽  
F. Profumo ◽  
A. Tenconi
Keyword(s):  
Induction Motor ◽  
Direct Torque Control ◽  
Motor Drives ◽  
Torque Control ◽  
Induction Motor Drives ◽  
Three Phase

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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