scholarly journals Speed Estimation of Three Phase Induction Motor Using Artificial Neural Network

2014 ◽  
Vol 3 (2) ◽  
pp. 52 ◽  
Author(s):  
Moinak Pyne
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Speed Estimation ◽  
Three Phase ◽  
Artificial Neural

scholarly journals Application Of Artificial Neural Network In The Efficient Control Of Three-phase Induction Motor

2010 ◽  
Vol 15 (2) ◽  
pp. 87-96 ◽  
Author(s):  
Arineu F. dos Santos ◽  
Francisco de Assis dos Santos Neves ◽  
Ronaldo Ribeiro Barbosa de Aquino ◽  
Marcelo Cabral Cavalcanti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Three Phase ◽  
Efficient Control ◽  
Artificial Neural

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.

scholarly journals Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network

2018 ◽  
Vol 5 (2) ◽  
pp. 138
Author(s):  
MUHAMMAD RUSWANDI DJALAL ◽  
KOKO HUTORO ◽  
ANDI IMRAN
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Induction Motors ◽  
Network Control ◽  
Neural Network Control ◽  
Backpropagation Neural Network ◽  
Three Phase ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRAKBanyak strategi kontrol berbasis kecerdasan buatan telah diusulkan dalam penelitian seperti Fuzzy Logic dan Artificial Neural Network (ANN). Tujuan dari penelitian ini adalah untuk mendesain sebuah kontrol agar kecepatan motor induksi dapat diatur sesuai kebutuhan serta membandingkan kinerja motor induksi tanpa kontrol dan dengan kontrol. Dalam penelitian ini diusulkan sebuah metode artificial neural network untuk mengontrol kecepatan motor induksi tiga fasa. Kecepatan referensi motor diatur pada kecepatan 140 rad/s, 150 rad/s, dan 130 rad/s. Perubahan kecepatan diatur pada setiap interval 0.3 detik dan waktu simulasi maksimum adalah 0,9 detik. Kasus 1 tanpa kontrol, menunjukkan respon torka dan kecepatan dari motor induksi tiga fasa tanpa kontrol. Meskipun kecepatan motor induksi tiga fasa diatur berubah pada setiap 0,3 detik tidak akan mempengaruhi torka. Selain itu, motor induksi tiga fasa tanpa kontrol memiliki kinerja yang buruk dikarenakan kecepatan motor induksi tidak dapat diatur sesuai dengan kebutuhan. Kasus 2 dengan control backpropagation neural network, meskipun kecepatan motor induksi tiga fasa berubah pada setiap 0.3 detik tidak akan mempengaruhi torsi. Selain itu, kontrol backpropagation neural network memiliki kinerja yang baik dikarenakan kecepatan motor induksi dapat diatur sesuai dengan kebutuhan.Kata kunci: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor.ABSTRACTMany artificial intelligence-based control strategies have been proposed in research such as Fuzzy Logic and Artificial Neural Network (ANN). The purpose of this research was design a control for the induction motor speed that could be adjusted as needed and compare the performance of induction motor without control and with control. In this research, it was proposed an artificial neural network method to control the speed of three-phase induction motors. The reference speed of motor was set at the rate of 140 rad / s, 150 rad / s, and 130 rad / s. The speed change was set at every 0.3 second interval and the maximum simulation time was 0.9 seconds. Case 1, without control, shows the torque response and velocity of three-phase induction motor without control. Although the speed of three phase induction motor was set to change at every 0.3 seconds, it would not affect the torque. The uncontrolled three-phase induction motors had poor performance due to induction motor speeds could not be adjusted as needed. Case 2 with backpropagation neural network control, although the speed of three phase induction motor changing at every 0.3 seconds would not affect the torque. In addition, the backpropagation neural network control had a good performance because the speed of induction motor could be adjusted as needed.Keywords: Backpropagation Neural Network (BPNN), NN Training, NN Testing, Motor

scholarly journals Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2176 ◽  
Author(s):  
Narongrit Pimkumwong ◽  
Ming-Shyan Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Torque Control ◽  
Control Technique ◽  
Speed Estimation ◽  
Speed Sensorless ◽  
Stator Current ◽  
Artificial Neural

This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow–Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high- and low-speed regions have confirmed the validity of the proposed speed estimation method.

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