ANN-Based Improved Direct Torque Control of Open-End Winding Induction Motor

被引：4

作者：

Lodi, Kaif Ahmed ^{[1
]}

Beig, Abdul R. ^{[1
]}

Al Jaafari, Khaled Ali ^{[1
]}

Aung, Zeyar ^{[2
]}

机构：

[1] Khalifa Univ, Dept Elect Engn, Adv Power & Energy Ctr, Abu Dhabi 127788, U Arab Emirates

[2] Khalifa Univ, Dept Comp Sci, Abu Dhabi 127788, U Arab Emirates

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2024年 / 71卷 / 10期

关键词：

Artificial neural network (ANN); direct torque control (DTC); open-end winding induction motor (OEWIM); torque ripple; MAGNET SYNCHRONOUS MOTOR; DTC SCHEME; RIPPLE; DRIVES; FREQUENCY; MACHINES; PMSM;

D O I：

10.1109/TIE.2024.3357865

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article presents an improved artificial neural network (ANN)-based direct torque control (DTC) algorithm for open-end winding induction motor (OEWIM) sensorless drives. The ANN replaces the traditional look-up table and serves as an efficient data storage system, allowing for optimal voltage vector selection based on small values of torque error and flux error for every angular position of the stator flux vector. By replacing the hysteresis controllers, improved torque and flux tracking are achieved with improved transient response. The proposed ANN-based DTC is verified through simulation as well as experimental tests. The experimental results and comparative results are presented.

引用

页码：12030 / 12040

页数：11

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