Advanced direct torque control based on neural tree controllers for induction motor drives

被引:1
|
作者
Aissa, Oualid [1 ]
Reffas, Abderrahim [2 ]
Krama, Abdelbasset [3 ,5 ]
Benkercha, Rabah [4 ]
Talhaoui, Hicham [1 ]
Abu-Rub, Haitham [5 ]
机构
[1] Univ Mohamed El Bachir El Ibrahimi Bordj Bou Arrer, Fac Sci & Technol, LPMRN Lab, Bordj Bou Arreridj, Algeria
[2] Univ Mohamed El Bachir El Ibrahimi Bordj Bou Arrer, Dept Electromech Engn, Bordj Bou Arreridj, Algeria
[3] Hamad Bin Khalifa Univ, Qatar Environm & Energy Res Inst, Doha, Qatar
[4] Univ Medea, LREA Lab, Medea, Algeria
[5] Texas A&M Univ Qatar, Dept Elect & Comp Engn, Doha, Qatar
关键词
Induction motor; Direct torque control; Artificial neural network; Decision tree; Virtex; 6; FPGA; OPAL-RT; 5600; C4.5 DECISION TREE; TABLE-BASED DTC; POWER QUALITY; PERFORMANCE; RIPPLE; MACHINES;
D O I
10.1016/j.isatra.2024.03.017
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 paper introduces a novel direct torque control approach based on the decision tree (T-DTC), employing artificial neural networks that are effectively trained to enhance accuracy and robustness. The main objective of T-DTC is the substantial reduction of flux and torque ripples inherent in the conventional DTC, ensuring effective control of the induction motor. The conventional hysteresis controllers for stator flux and electromagnetic torque are replaced by two advanced controllers named M5 Prime model trees. Additionally, the traditional switching table is substituted with a novel decision tree table utilizing the classifier algorithm 4.5. The effectiveness of the proposed T-DTC strategy is demonstrated through simulation in MATLAB/Simulink and validated in real-time using an HIL platform based on OPAL-RT OP 5600 and Virtex 6 FPGA ML605. The results obtained demonstrate a notable improvement compared to existing techniques in the literature.
引用
收藏
页码:92 / 104
页数:13
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