NDLI: Rotor position estimation of 6/4 Switched Reluctance Motor using a novel neural network algorithm

Content Provider	IEEE Xplore Digital Library
Author	Beno, M.M. Rajaji, L. Varatharaju, V.M. Santos, A.N.
Copyright Year	2011
Description	Author affiliation: Engineering Department, Ibra College of Technology Ministry of Manpower, Sultanate of Oman (Beno, M.M.; Rajaji, L.; Varatharaju, V.M.; Santos, A.N.)
Abstract	This paper presents a novel approach for estimating the rotor position of a Switched Reluctance Motor (SRM) drive system using the Cascade Correlation Artificial Neural Network Algorithm (CCNNA). This technique estimates rotor position by measuring the three-phase voltages and currents and using magnetic characteristics of the SRM, with the aid of an ANN. The rotor position estimating technique is 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 technique for a 6/4 pole SRM using the cascade correlation algorithm-based ANN.
Starting Page	77
Ending Page	80
File Size	365489
Page Count	4
File Format	PDF
ISBN	9781612841182
e-ISBN	9781612841199
DOI	10.1109/IEEEGCC.2011.5752630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-02-19
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Computational modeling Cascade Correlation Rotors Error Analysis Artificial neural networks Switches Switched Reluctance Motor (SRM) Rotor Position Estimation Reluctance motors Artificial Neural Network (ANN)
Content Type	Text
Resource Type	Article

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