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Joint State and Parameter Estimation of Squirrel Cage Induction Motor – A System Identification Approach using EM based Extended Kalman Filter

by K.Radhakrishnan, A.Unnikrishnan, K.G.Balakrishnan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 1
Year of Publication: 2011
Authors: K.Radhakrishnan, A.Unnikrishnan, K.G.Balakrishnan
10.5120/3267-4426

K.Radhakrishnan, A.Unnikrishnan, K.G.Balakrishnan . Joint State and Parameter Estimation of Squirrel Cage Induction Motor – A System Identification Approach using EM based Extended Kalman Filter. International Journal of Computer Applications. 27, 1 ( August 2011), 24-29. DOI=10.5120/3267-4426

@article{ 10.5120/3267-4426,
author = { K.Radhakrishnan, A.Unnikrishnan, K.G.Balakrishnan },
title = { Joint State and Parameter Estimation of Squirrel Cage Induction Motor – A System Identification Approach using EM based Extended Kalman Filter },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 1 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number1/3267-4426/ },
doi = { 10.5120/3267-4426 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:40.585578+05:30
%A K.Radhakrishnan
%A A.Unnikrishnan
%A K.G.Balakrishnan
%T Joint State and Parameter Estimation of Squirrel Cage Induction Motor – A System Identification Approach using EM based Extended Kalman Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 1
%P 24-29
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper deals with the recursive optimum estimation of rotor resistance, inductance and stator resistance of induction motor. The estimation of parameter and states in the presence of system noise is achieved using EKF, which takes in to account measurement and modelling inaccuracies. A major limitation in the parameter estimation using EKF is that its optimality is dependent on the choice of the right covariance matrices. In this paper the EM(Expectation Maximization) algorithm along with the Kalman smoothing is used to obtain the initial values of process and measurement covariance matrices. The Vas model of the induction motor is used for simulation and the rotor inductance, rotor resistance and stator resistance are considered as parameters as well as states. For the estimation of these parameters, the state vector is augmented with these parameters to be estimated. The parameters used for simulation is that of a three phase, 2.2 kw, 400V, 50 Hz cage induction motor. Matlab is used to simulate the system and from the results it is observed that the parameter estimates converges to true values with a reasonable degree of accuracy.

References
  1. R.Krishnan, Electric Motor Drives ( Pearson Education, Singapore, 2003).
  2. P.Vas, Vector Control of AC Machines ( Oxford University Press, New York, 1994).
  3. K. Radhakrishnan, A. Unnikrishnan and K.G. Balakrishnan, Model Recognition for Estimation of Direction of Arrival using Extended Kalman Filter, Proceedings of the Third IASTED International Conference on Signal Processing , Pattern Recognition, and Applications (SPPRA), February 2006,
  4. M.A. Ouhrouche, EKF Based On-line Tuning of Rotor Time Constant in an Induction Motor Vector Control, International Journal of Power and Energy Systems, 20(2), 2003, 103-110.
  5. L.Salvatore, S.Stasi, and L.Tarchioni, A New EKF Based Algorithm for Flux Estimation in Induction Machines, IEEE Transactions on Industrial Electronics, 40(5),1997,496-504.
  6. S.Wade, M.W. Dunnigan and B.W. Williams, Modeling and Simulation of Induction Machine Vector Control with Rotor resistance Identification, IEEE Transactions on Power Electronics, 121(3), 1997,495-506.
  7. L.C. Zai, C.L. Demacro, and T.A.Lipo, An Extended Kalman Filter Approach to Rotor Time Constant Measurement in PWM Induction Motor Drives , IEEE Transactions on Industry Applications, 28(1), 1992,96-104.
  8. R.E.Kalman, R.S.Bucy, New Results in Linear Filtering and Prediction Theory, Journal of Basic Engineering, 1961,95-108.
  9. C.K.Chui and Chen, Kalman Filtering with Real Time Applications, Springer Verlag, New York,1991,108-118
  10. A.H. Jaswinski, Stochastic Processes and Filtering Theory, Academic Press,1970.
  11. A.P. Dempster, N.M Laird and D.B Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm , Journal of the Royal Statistical Society Series, B 39,1977,1-38.
  12. V.Digalakis, J.R. Rohlicek, M.Ostendorf, ML Estimation of Stochastic Linear System with EM Algorithm and its Application to Speech Recognition, IEEE Transactions on Speech and Audio Processing , 1, 1993,431-442.
  13. Shan-Shu Xiong, Zaho- Yug, Neural Filtering of Coloured Noise Based on Kalman Filter Structure, IEEE Transaction on Instrumentation and Measurement, 52, 2003,742-747.
  14. Roberto Togneri, Li Deng, Joint State and Parameter Estimation for Target Directed Non-Linear Dynamic System Model, IEEE transaction on Signal Processing, 51, 2003,3061-3070.
  15. A Gelb, Applied Optimal Estimation. MIT Press, 1981.
Index Terms

Computer Science
Information Sciences

Keywords

Kalman filter Induction motor

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