Elman neural network-based nonlinear state estimation for induction motors

Authors : Saadettin AKSOY, Aydın MÜHÜRCÜ

Pages : 861-875

View : 22 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This study presents a recurrent neural network insert ignore into journalissuearticles values(RNN);-based nonlinear state estimator that uses an Elman neural network structure insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(EKF); in terms of accuracy and convergence speed.
Keywords : Induction motor, state estimation, extended Kalman filtering, recurrent neural networks