Standstill Estimation of Stator Resistance of Induction Motors with Novel Innovation-Based Adaptive Extended Kalman Filter

Abstract

In this study, a method is developed to identify stator resistance of an induction motor (IM) at standstill in the self-tuning. An innovation-based adaptive extended Kalman filter (IAEKF) estimator in which the process noise is dynamically updated with an adaptive mechanism different from the conventional extended Kalman filter (EKF) is designed to estimate stator resistance with αβ- stator stationary axis components of stator current and αβ- components of stator flux of an IM. The reason for estimating the stator flux and stator current together with the stator resistance is to both increase the stability of the proposed estimator algorithm by using the correlation between the parameters and states in the non-linear inputs applied to the estimator and obtain the motor flux information needed by the control system. In the proposed IAEKF algorithm, a stator flux-based IM model is used for prediction purposes. The standstill estimation performance of the proposed novel IAEKF is tested with both sinusoidal and PWM power supplies, The real-time estimation results show the effectiveness and prediction accuracy of the proposed stochastic-based estimator.