Wireless Sensor Network for Temperatures Estimation in an Asynchronous Machine Using a Kalman Filter

Abstract

A 4^th-order Kalman filter (KF) algorithm is developed based on the thermal model of an asynchronous machine. The thermal parameters are identified and KF is implemented in a wireless sensor network (WSN) to estimate the temperatures of the stator windings, the rotor cage, and the stator core of an asynchronous machine. The rotor speed, coolant air temperature, and the effective current and voltage are acquired by a WTIM (wireless transducer interface module) separately and transmitted to a NCAP (network capable application processor) where the KF algorithm is implemented. Losses of the stator windings and the rotor cage are copper losses, and the stator core losses are iron losses. The losses of the stator windings, the rotor cage and the stator core are calculated from the measurements and are processed with the coolant air temperature by KF. As the resistance varies from temperature, the estimated temperature of the stator windings is used for compensating the rising of resistance.  Simulations and experiments on the test bench were performed before the KF algorithm is implemented on a wireless sensor node. The real-time temperature estimator on WSN is independent of control algorithm and can work under any load condition with very high accuracy.