scholarly journals Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

Sensors ◽  
2015 ◽  
Vol 15 (7) ◽  
pp. 15311-15325 ◽  
Author(s):  
Jose Gutierrez-Villalobos ◽  
Juvenal Rodriguez-Resendiz ◽  
Edgar Rivas-Araiza ◽  
Moisés Martínez-Hernández
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Line Speed ◽  
Rotor Resistance ◽  
On Line ◽  
Artificial Neural

scholarly journals Sensorless Direct Power Control of Induction Motor Drive Using Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Abolfazl Halvaei Niasar ◽  
Hossein Rahimi Khoei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Control ◽  
Induction Motor ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Direct Power ◽  
Direct Power Control ◽  
Artificial Neural ◽  
Propagation Network

This paper proposes the design of sensorless induction motor drive based on direct power control (DPC) technique. It is shown that DPC technique enjoys all advantages of pervious methods such as fast dynamic and ease of implementation, without having their problems. To reduce the cost of drive and enhance the reliability, an effective sensorless strategy based on artificial neural network (ANN) is developed to estimate rotor’s position and speed of induction motor. Developed sensorless scheme is a new model reference adaptive system (MRAS) speed observer for direct power control induction motor drives. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Simulink. Some simulations are carried out for the closed-loop speed control systems under various load conditions to verify the proposed methods. Simulation results confirm the performance of ANN based sensorless DPC induction motor drive in various conditions.

Artificial Neural Network Based Sensorless Vector Control of Induction Motor Drive

2014 ◽  
Vol 704 ◽  
pp. 325-328 ◽  
Author(s):  
Abolfazl Halvaei Niasar ◽  
Hossein Rahimi Khoei ◽  
Mahdi Zolfaghari ◽  
Hassan Moghbeli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
High Reliability ◽  
Motor Drive ◽  
Speed Estimation ◽  
Induction Motor Drive ◽  
Artificial Neural ◽  
Speed Estimator ◽  
Propagation Network

Controlled induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. For these speed sensorless AC drive system, it is key to realize speed estimation accurately. This paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab. Simulation result shows a good performance of speed estimator. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to resistances of stator variations.

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