Rotor Temperature Virtual Sensing for Induction Machines Using a Lumped-Parameter Thermal Network and Dual Kalman Filtering

Abstract

Accurate knowledge of the induction machine rotor temperature is important for both condition monitoring and motor performance. Lumped-parameter thermal networks allow to flexibly model the temperature distribution with low computational effort. However, their accuracy highly depends on the accuracy of their thermal parameters. Moreover, these parameters are determined offline and held constant during operation. In this article, a rotor temperature virtual sensing strategy is proposed, combining a lumped-parameter thermal network with stator windings temperature measurements. The latter measurement data can be easily collected and together with the thermal network serve as input to a dual Kalman filter to estimate both the rotor temperature evolutions and the thermal model parameters. The advantage of the presented approach is that the thermal model does not need exact identification for the considered motor. One can start from a thermal model identified from intrusive rotor measurement data on a comparable motor that is subsequently transferred to the motor to be monitored. This results in a fast and convenient way to estimate the rotor temperature in real-time. Experimental results on a 4-pole 5.5 kW induction machine demonstrate the validity of the presented strategy to accurately monitor the rotor temperature within an error range of approximately ±6 °C.