Sensorless Cascaded-Model Predictive Control applied to a Doubly Fed Induction Machine

Abstract

This paper proposes a sensorless cascaded model predictive control strategy applied to a doubly-fed induction machine. This technique is based on an improved stator flux estimator, and an extended Kalman filter to control encoder-less and independently the electromagnetic torque and the reactive power of the machine. The purpose of employing a model predictive-based control, is to achieve fast dynamic response and upgrading it with a modulation stage to mitigate the control variables ripple. The introduced control technique might be considered for adjustable speed application such as wind energy conversion systems.