Authors can submit their research articles to editor@ijsea.com  
Untitled Document

Confernces

IJSEA is index with

 

 

 

 

 

 

 

 

IJSEA Archive (Volume 6, Issue 10)

International Journal of Science and Engineering Applications (IJSEA)  (Volume 6, Issue 10 October 2017)

Prediction of Excitation Angles for a Switched Reluctance Generator using Artificial Neural Network

Pairote Thongprasri





 PDF 



Keywords: artificial neural network; feed-forward; back-propagation; hidden layer; output layer

Abstract References BibText


        This paper presents a method to determine excitation angles for a Switched Reluctance Generator (SRG) by using Artificial Neural Network (ANN). The ANN model consists of the feed-forward neural network and the back-propagation learning with a linear activation function (the linear function) and a nonlinear function (the hyperbolic tangent). The ANN model with two layers; the hidden layer and the output layer, is derived from the current and flux linkage of the SRG. The SRG model is built from the magnetization curve which the flux linkage versus current at different rotor positions is analyzed from the finite element method (FEM). An 8/6 SRG was set up to validate the proposed ANN method.


[1] K.M. Rahman, S. Gopalakrishnan, Optimized, Instan-taneousTorque Control of Switched Reluctance Motor by Neural Network, in Proc. IEEE IAS, vol. 37, 2001, pp. 904-913.
[2] E. Mese, D.A. Torrey, An approach for Sensorless Posi-tion Estimation for Switched Reluctance Motors Using Artificial Neural Networks, IEEE Trans. Power Elec-tronics, vol.17. no. l, 2002, pp. 66-75.
[3] P. Asadi, M. Ehsani, and B. Fahimi, Design and control characterization of switched reluctance generator for maximum output power, in Proc. IEEE APEC, 2006, pp. 1639–1644.
[4] R.G. Lopez and B. Diong, “Simplified control of switched reluctance machines for AC generation,” in Proc. IEEE IAS, vol. 1, 2004, pp. 409.
[5] K. Yilmaz, E. Mese, A. Cengiz, Minimum Inductance Estimation In Switched Reluctance Motors By Using Artificial Neural Networks, in Proc. IEEE MELECON, 2002, pp. 152-156.
[6] H. Haykin, Neural Networlrs;A comprehensive Founda-tion, IEEE press, Macmillan College Publish Comp.,l994
[7] J.M. Zurada, Introduction to Artificial Neural Systems, PWS Publishing Company, 1992.


@article{Pairote06101001,
title = " Curriculum Delivery Constraints of Arabic Language as a Foreign Language in the UAE ",
journal = "International Journal of Science and Engineering Applications (IJSEA)",
volume = "6",
number = "9",
pages = "296 - 301 ",
year = "2017",
author = " Pairote Thongprasri ",
}