scholarly journals Speed and Rotor Resistance Estimation of AC Induction Motor

2018 ◽  
Vol 173 ◽  
pp. 02037
Author(s):  
Wang Dafang ◽  
Wang Miaoran ◽  
Dong Guanglin ◽  
Wei Hui ◽  
Xu Zexu
Keyword(s):  
Induction Motor ◽  
Reactive Power ◽  
Estimation Method ◽  
Adaptive Controller ◽  
High Accuracy ◽  
Identification Accuracy ◽  
Speed Sensorless ◽  
Rotor Resistance ◽  
Model Reference Adaptive ◽  
Ac Induction Motor

The speed estimation method based on Extended Kalman Filter (EKF) is widely used in speed sensorless induction motor. The method has high accuracy and good robustness. However, EKF is sensitive to parameters. In particular, the rotor resistance becomes up to twice the original when the motor is running. This greatly affects the speed identification accuracy. This paper achieves online estimation of rotor resistance utilizing the Model Reference Adaptive Controller (MRAC) based on reactive power. This ensures that the speed is estimated with high accuracy even if the rotor resistance changes suddenly. Simulation results have been presented to verify the effectiveness of the method.

Speed-Sensorless Vector Control Based on ANN MRAS for Induction Motor Drives

2015 ◽  
Vol 19 (1) ◽  
pp. 127-133 ◽  
Author(s):  
Kai Xu ◽  
◽  
Shanchao Liu
Keyword(s):  
Neural Networks ◽  
Vector Control ◽  
Induction Motor ◽  
Reactive Power ◽  
Motor Drives ◽  
Induction Motor Drives ◽  
Speed Sensorless ◽  
Zero Crossing ◽  
Common Strategy ◽  
Model Reference Adaptive

In the speed-sensorless induction motor drives system, Model Reference Adaptive System (MRAS) is the most common strategy. However, speed estimation using reactive power based MRAS has the problem of instability in the regenerating mode of operation. Such estimation technique is simple and has several notable advantages, but is not suitable for induction motor drives. To overcome these problems, a suitable Artificial Neural Networks (ANN) is presented to replace the adjustable model to make the system stable when working at low speed and zero crossing. Simultaneously, in order to enhance the ANN convergence speed and avoid the trap of local minimum value of algorithm, we used themodified Particle Swarm Optimization (PSO) to optimize the weights and threshold values of neural networks. Then the ANN-based MRAS was used to identify the speed of motor in the indirect vector control system. The results of the simulation show that, by this method, the speed of motor can be identified accurately in different situations, and the result is reliable.

scholarly journals Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

2018 ◽  
Vol 8 (5) ◽  
pp. 2691
Author(s):  
Mini R ◽  
Shabana Backer P. ◽  
B. Hariram Satheesh ◽  
Dinesh M. N
Keyword(s):  
Comparative Analysis ◽  
Induction Motor ◽  
Reactive Power ◽  
Adaptive Controller ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Loop Model ◽  
Low Speed ◽  
Rotor Flux

<p>This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions.</p>

scholarly journals Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

2020 ◽  
Vol 20 (3) ◽  
pp. 1653
Author(s):  
M.Z. Ismail ◽  
M.H.N. Talib ◽  
Z. Ibrahim ◽  
J. Mat Lazi ◽  
Z. Rasin
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Adaptive Controller ◽  
Tuning Parameter ◽  
Model Reference ◽  
Wide Range ◽  
Speed Performance ◽  
Self Tuning ◽  
Model Reference Adaptive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the   speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>

Sign in / Sign up

Export Citation Format

Share Document