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AI Based Rotor Position Estimation Techniques for Switched Reluctance Motor Drives

M. Marsaline Beno*, N.S. Marimuthu**

* Senior Lecturer, St. Xavier's Catholic College of Engineering, Nagercoil, TamilNadu, India.

** Professor and Head, Department of Electrical and Electronics Engineering, Government College of Engineering, Tirunelveli, TamilNadu, India.

Periodicity:January - March'2008
DOI : https://doi.org/10.26634/jee.1.3.423

Abstract

This paper presents artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) based rotor position estimation techniques for switched reluctance motor (SRM) drive system. The techniques estimate rotor position by measuring the three-phase voltages and currents and using magnetic characteristics of the SRM, with the aid of an ANN and ANFIS. The rotor position estimating techniques are used in a high-performance sensor less variable speed SRM drive. The results are compared with the measured values, and the error analyses are given to determine the performance of the developed method. The error analyses have shown great accuracy and successful rotor position estimation techniques for a 6/4 poleswitched reluctance motor using AI techniques.

Keywords

Adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), sensor less operation, switched reluctance motor (SRM).

How to Cite this Article?

M. Marsaline Beno and N.S. Marimuthu (2008). AI Based Rotor Position Estimation Techniques for Switched Reluctance Motor Drives. i-manager’s Journal on Electrical Engineering, 1(3), Jan-Mar 2008, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 58-63. https://doi.org/10.26634/jee.1.3.423

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