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

S. Usha, C. Subramani and Sanjeevikumar Padmanaban
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S. Usha: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
C. Subramani: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
Sanjeevikumar Padmanaban: Center for Bioenergy and Green Engineering, Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark

Energies, 2019, vol. 12, issue 5, 1-25

Abstract: 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.

Keywords: induction motor; speed estimation; model reference adaptive system; kalman filter; luenberger observer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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