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Reseach Article

Optimal Parameters Estimation of a Switched Reluctance Motor by Kohonen�s Self Organizing Feature Map Method

Published on None 2011 by B.Jaganathan, R.Brindha, Sumit Kumar Sah
Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 2
None 2011
Authors: B.Jaganathan, R.Brindha, Sumit Kumar Sah
9cbe39cd-4ae0-4ca2-a16c-d2bc4664c5b4

B.Jaganathan, R.Brindha, Sumit Kumar Sah . Optimal Parameters Estimation of a Switched Reluctance Motor by Kohonen�s Self Organizing Feature Map Method. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 2 (None 2011), 24-28.

@article{
author = { B.Jaganathan, R.Brindha, Sumit Kumar Sah },
title = { Optimal Parameters Estimation of a Switched Reluctance Motor by Kohonen�s Self Organizing Feature Map Method },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 2 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 24-28 },
numpages = 5,
url = { /specialissues/ait/number2/2832-213/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A B.Jaganathan
%A R.Brindha
%A Sumit Kumar Sah
%T Optimal Parameters Estimation of a Switched Reluctance Motor by Kohonen�s Self Organizing Feature Map Method
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 2
%P 24-28
%D 2011
%I International Journal of Computer Applications
Abstract

SRM drives are the upcoming drives nowadays as these have many advantages such as simplicity , low manufacturing and operating costs, fault tolerance, high torque/inertia ratio and efficiency. The estimation of SRM drive parameters is an important consideration in their field. Many methods are available for this. However the estimation of the optimal parameters is normally preferred. Making use of neural networks is one of the best ways to achieve this. This paper proposes an unsupervised learning method i.e., Kohonen’s Self Organizing Feature Map method of estimation of SRM drives. Since the method makes use of ‘winner takes all’ of a neuron, the values obtained by this, will be the optimal values. The drive is first simulated and the parameters obtained are used for training the ANN. The Unsupervised learning method is the Kohonen’s Self Organizing Feature Map method, which is used for the estimation of the SRM drive parameters. The parameters estimated are the currents and fluxes in the two axis . Because of the unsupervised learning, it can be stated that the estimated values are the best or the optimal values. MATLAB/Simulink is used for the simulation and the results are shown.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network d-q control Epoch Estimation KSOFM SRM Optimal Parameters Unsupervised Learning Unit Vectors Weight Matrix Epoch Estimation KSOFM SRM Optimal Parameters Unsupervised Learning Unit Vectors Weight Matrix