An adaptive Kalman filtering approach to induction machine stator winding temperature estimation based on a hybrid thermal model

The stator winding temperature of an induction machine is estimated from either a thermal model-based or an induction machine parameter-based temperature estimator. The thermal model-based temperature estimator is simple and robust, but it is usually incapable of giving an accurate temperature estimate tailored to a specific motor's thermal capacity. The induction machine parameter-based temperature estimator is accurate and machine-dependent, but it is often too sensitive to the machine's parametric changes. For small to medium size mains-fed induction machines with TEFC design, a hybrid thermal model is proposed to unify these two temperature estimators. Based on this hybrid thermal model, an adaptive Kalman filter is then formulated to track the stator winding temperature with increased accuracy and robustness. Noise identification and input estimation techniques are used in the Kalman filter to obtain an optimal estimate of the stator winding temperature. The experimental results are given to validate the proposed scheme. The overall algorithm provides efficient and accurate tracking of the stator winding temperature, ensures safe and reliable motor operation and avoids nuisance overload trips, all without using any real temperature sensors.