DSP implementation of an artificial neural network for induction motor control

2002 ◽  
Author(s):  
M. Mohamadian ◽  
E.P. Nowicki ◽  
A. Chu ◽  
F. Ashrafzadeh ◽  
J.C. Salmon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Control ◽  
Induction Motor ◽  
Dsp Implementation ◽  
Artificial Neural

Neural Network Based Monitoring, Protection & Fault Detection of Induction Motor Using PLC

2018 ◽  
Vol 7 (2.24) ◽  
pp. 140
Author(s):  
Devendra Somwanshi ◽  
Arvind Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Control ◽  
Induction Motor ◽  
Control Algorithm ◽  
Network Control ◽  
Soft Starter ◽  
Artificial Neural ◽  
Commercial Applications ◽  
Control Regime

Induction motors used mostly in industrial, commercial applications & are seldom denominated power horse of industry. To reduce the motor starting current soft starter requirement is increasing day by day & to maintain the torque proportionally with the load requirement. Now intelligent soft starters evolved to improve the motor starting. This work is comprised of development of an Artificial Neural Network control regime for closed loop of induction motor. The same has been achieved using a standard 0.75 KW three phase induction motor using Matlab, PLC, SCADA & DRIVE. The Artificial Neural Network scheme is compared with traditional Proportional control regime. We have observed that the performance of ANN Induction Motor control Algorithm has been 14-21 % better than only Proportional Motor Control algorithm.  

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

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.

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