Induction motor, state estimation, extended Kalman filtering, recurrent neural networks

Authors

SAADETTİN AKSOY
AYDIN MÜHÜRCÜ

DOI

10.3906/elk-1002-406

Abstract

This study presents a recurrent neural network (RNN)-based nonlinear state estimator that uses an Elman neural network structure (ENN) for state estimation of a squirrel-cage induction motor. The proposed algorithm only uses the measurements of the stator currents and the rotor angular speed, and it learns the dynamic behavior of the state observer from these measurements through prediction error minimization. A squirrel-cage induction motor was fed from sinusoidal, 6-step, and pulse-width modulation (PWM) supply sources at different times in order to observe the performance of the proposed estimator for different operation conditions. Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy and convergence speed.

Keywords

Induction motor, state estimation, extended Kalman filtering, recurrent neural networks

First Page

861

Last Page

875

Recommended Citation

AKSOY, SAADETTİN and MÜHÜRCÜ, AYDIN (2011) "Induction motor, state estimation, extended Kalman filtering, recurrent neural networks," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 19: No. 6, Article 4. https://doi.org/10.3906/elk-1002-406
Available at: https://journals.tubitak.gov.tr/elektrik/vol19/iss6/4