Low Speed Operation of Sensorless Estimators for Induction Machines using Extended, Unscented and Cubature Kalman Filter Techniques

Abstract

In this study, three feasible speed sensorless estimators of induction machines are presented by using extended, unscented and cubature Kalman filter algorithms. The estimators are based on an augmented non-linear state-space model of these machines, which describes the dynamics in stationary reference frame with six state variables. As an important part of the estimator design, an observability study is provided for the nonlinear model and an observability condition is formulated as well. The estimators are compared experimentally around zero stator frequency with respect to the speed estimation performance. However, the estimators are investigated only in open-loop and without external load disturbance.