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Abstract
This paper presents a model-based fault detection approach for induction motors. A
new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter
(EKF) is utilized as a state estimation tool for on-line detection of broken bars
in induction motors based on rotor parameter value estimation from stator current
and voltage processing. The hypothesis on which the detection is based is that the
failure events are detected by jumps in the estimated parameter values of the model.
Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking
a bar the estimated rotor resistance is increased instantly, thus providing two values
of resistance after and before bar breakage. In order to compare the estimation performance
of the EKF and UKF, both observers are designed for the same motor model and run with
the same covariance matrices under the same conditions. Computer simulations are carried
out for a squirrel cage induction motor. The results show the superiority of UKF over
EKF in nonlinear system (such as induction motors) as it provides better estimates
for rotor fault detection.
Copyright 2010. Published by Elsevier Ltd.