Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive

被引：7
|
作者：
Usha, S. [1 ]
Subramani, C. [1 ]
Padmanaban, Sanjeevikumar [2 ]
机构：
[1] SRM Inst Sci & Technol, Dept Elect & Elect Engn, Kattankulathur 603203, India
[2] Aalborg Univ, Dept Energy Technol, Ctr Bioenergy & Green Engn, DK-6700 Esbjerg, Denmark
来源：
ENERGIES | 2019年 / 12卷 / 05期
关键词：
induction motor; speed estimation; model reference adaptive system; kalman filter; luenberger observer; PARAMETER-IDENTIFICATION; MACHINES; STABILITY;
D O I：
10.3390/en12050920
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
This paper proposes the modified, extended Kalman filter, neural network-based model reference adaptive system and the modified observer technique to estimate the speed of a five-phase induction motor for sensorless drive. The proposed method is generated to achieve reduced speed deviation and reduced torque ripple efficiently. In inclusion, the result of speed performance and torque ripple under parameter variations were analysed and compared with the conventional direct synthesis method. The speed estimation of a five-phase motor in the four methods is analysed using MATLAB Simulink platform, and the optimum method is recognized using time domain analysis. It is observed that speed error is minimized by 60% and torque ripple is reduced by 75% in the proposed method. The hardware setup is carried out for the optimized method identified.
引用
收藏
页数：25
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