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Reseach Article

Design of Derivative-free State Estimators for a Three Phase Induction Motor ñ A Comparative Study

by J. Ravikumar, S. Subramanian, J. Prakash
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 2
Year of Publication: 2011
Authors: J. Ravikumar, S. Subramanian, J. Prakash
10.5120/3538-4837

J. Ravikumar, S. Subramanian, J. Prakash . Design of Derivative-free State Estimators for a Three Phase Induction Motor ñ A Comparative Study. International Journal of Computer Applications. 29, 2 ( September 2011), 15-24. DOI=10.5120/3538-4837

@article{ 10.5120/3538-4837,
author = { J. Ravikumar, S. Subramanian, J. Prakash },
title = { Design of Derivative-free State Estimators for a Three Phase Induction Motor ñ A Comparative Study },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 2 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 15-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number2/3538-4837/ },
doi = { 10.5120/3538-4837 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:45.759360+05:30
%A J. Ravikumar
%A S. Subramanian
%A J. Prakash
%T Design of Derivative-free State Estimators for a Three Phase Induction Motor ñ A Comparative Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 2
%P 15-24
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Particle filters are an alternative to approximate the Kalman filter for nonlinear problems. This paper intends to assess the potential of Particle Filter (PF) and its variants in the context of the state estimation problem of a three phase induction motor. The conventional Particle Filter (SIR-PF), and particle filters that employ importance sampling through proposal distributions such as Particle Filter with Extended Kalman Filter (PF-EKF) and Particle Filter with Unscented Kalman Filter (PF-UKF), which are proposed in the literature within the particle filtering framework that takes into account of the latest observational information to reduce the risk of weight degeneracy is described and the error behaviour is analyzed through Monte Carlo simulations with regard to three scenarios Viz., low speed operation, step changes in load torque and reversal of speed. Simulation results demonstrate the superior tracking performance of PF-EKF at the expense of higher computational effort over the other approaches and can be determined to be a good substitute for the UKF in terms of accuracy of the state vector estimation.

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Index Terms

Computer Science
Information Sciences

Keywords

Unscented Kalman Filter (UKF) Particle Filter with EKF as Proposal Distribution [PF-EKF] Particle Filter with UKF as Proposal Distribution [PF-UKF] Sampling Importance Re-sampling Particle Filter [SIR-PF] Bayesian State Estimation Three Phase Induction motor [IM]