scholarly journals Neural Network Based Fault Diagnosis of Three Phase Inverter Fed Vector Control Induction Motor

2019 ◽  
Vol 63 (4) ◽  
pp. 295-305 ◽  
Author(s):  
Bilal Djamal Eddine Cherif ◽  
Azeddine Bendiabdellah ◽  
Mokhtar Bendjebbar ◽  
Amina Tamer
Keyword(s):  
Neural Network ◽  
Vector Control ◽  
Induction Motor ◽  
Control Strategy ◽  
Open Circuit ◽  
Average Value ◽  
Three Phase ◽  
Artificial Neural Network Ann ◽  
Three Phase Inverter ◽  
Vector Control Strategy

The paper investigates the detection and location of IGBT open-circuit faults in two-level inverter fed induction motor controlled by indirect vector control strategy. The investigation proposes two new approaches entirely based on the Artificial Neural Network (ANN) for the extraction of the exact fault angle corresponding to the IGBT switch open-circuit fault. The first approach (Approach1) based on the Clark currents transform calculates the average value of the Clark currents to find the exact fault angle θ. The second approach (Approach2) based directly on the three-phase stator currents (without any transformation) calculates the average value of the three-phase currents to determine the exact fault angle between the phases (θab, θbc, θca). The paper conducts also a comparative study between the two approaches to assess the merits of each one of them. Experimental work is conducted to illustrate the effectiveness of the techniques and validate the results obtained.

scholarly journals A Novel Vector Control Strategy for a Six-Phase Induction Motor with Low Torque Ripples and Harmonic Currents

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1102 ◽  
Author(s):  
Hamidreza Heidari ◽  
Anton Rassõlkin ◽  
Toomas Vaimann ◽  
Ants Kallaste ◽  
Asghar Taheri ◽  
...  
Keyword(s):  
Vector Control ◽  
Induction Motor ◽  
Control Strategy ◽  
High Performance ◽  
Direct Torque Control ◽  
Low Frequency ◽  
Torque Control ◽  
Harmonic Currents ◽  
Torque Ripples ◽  
Vector Control Strategy

In this paper, a new vector control strategy is proposed to reduce torque ripples and harmonic currents represented in switching table-based direct torque control (ST-DTC) of a six-phase induction motor (6PIM). For this purpose, a new set of inputs is provided for the switching table (ST). These inputs are based on the decoupled current components in the synchronous reference frame. Indeed, using both field-oriented control (FOC) and direct torque control (DTC) concepts, precise inputs are applied to the ST in order to achieve better steady-state torque response. By applying the duty cycle control strategy, the loss subspace components are eliminated through a suitable selection of virtual voltage vectors. Each virtual voltage vector is based on a combination of a large and a medium vector to make the average volt-seconds in loss subspace near to zero. Therefore, the proposed strategy not only notably reduces the torque ripples, but also suppresses the low frequency current harmonics, simultaneously. Simulation and experimental results clarify the high performance of the proposed scheme.

Vector Control System of Seven-Phase Induction Motor Based on DSP

2014 ◽  
Vol 513-517 ◽  
pp. 4421-4425
Author(s):  
Ji Ling Guo ◽  
Zhong Cai Qiu ◽  
Jian Xiao ◽  
Peng Luo
Keyword(s):  
Vector Control ◽  
Induction Motor ◽  
Control Strategy ◽  
Simulation Experiment ◽  
Motor Drives ◽  
Induction Motor Drives ◽  
Spatial Decomposition ◽  
Harmonic Components ◽  
Vector Control System ◽  
Vector Control Strategy

In this paper, a vector control strategy with UVM for seven-phase induction motor drives is presented and tested.Based on the spatial decomposition model of 7-phase induction motor, the d-q fundamental spatial components are controlled using vector control strategy. However the harmonic components of currents are generated by the harmonic components in the 3rd and 5th harmonic subspaces.Combined with voltages in harmonic subspaces set to be zero, the UVM with harmonic current elimination is proposed.Based on the simulation, experiment results verify the feasibility and effectiveness of the strategy proposed on the 7-phase IM experimental setup with DSP core TMS320F28335.

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

An Optimized Strategy Based on Hysteresis Space Vector Control of Three-Phase VSR

2013 ◽  
Vol 278-280 ◽  
pp. 79-85 ◽  
Author(s):  
Xiao Ju Chen ◽  
Ai Min Zhang ◽  
Jing Jing Huang ◽  
Jian Hua Wang
Keyword(s):  
Hysteresis Loop ◽  
Vector Control ◽  
Control Strategy ◽  
Fuzzy Rules ◽  
Switching Frequency ◽  
Space Vector ◽  
Three Phase ◽  
New Strategy ◽  
Current Error ◽  
Vector Control Strategy

In order to reduce the vast total harmonic distortion (THD) caused by hysteresis loop space vector control strategy in three-phase Voltage Source Rectifier (VSR), an optimized strategy based on fuzzy rules is proposed in the paper. In the new strategy, a current error vector threshold value was pre-set. When the actual current error value is less than the threshold, the hysteresis loop space vector control strategy will be adopted to reduce the switching frequency and loss. However, when the current error value is larger than the threshold, the optimized hysteresis loop control strategy based on fuzzy rules will be adopted to achieve the fast current tracking. Based on the new strategy, the simulation model has been established and simulated by matlab/simulink. And the results show that the optimized control strategy not only ensures the fast dynamic response with low switching frequency, but also reduces the current THD significantly. Finally, the feasibility and the superiority of the optimized strategy based on fuzzy rules have been further verified under a 1kW experiment system.

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