Detection of broken bars in induction motors using an extended Kalman filter for rotor resistance sensorless estimation

This paper deals with broken bars detection in induction motors. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. To detect broken bars, measurements of stator voltages and currents are processed by an extended Kalman filter for the speed and rotor resistance simultaneous estimation. In particular, rotor resistance is estimated and compared with its nominal value to detect broken bars. In the proposed extended Kalman filter approach, the state covariance matrix is adequacy weighted leading to a better states estimation dynamic. Its main advantage is the correct rotor resistance estimation even for an unloaded induction motor. As part of this estimation process, it is necessary to compensate for the thermal variation in the rotor resistance. Computer simulations, carried out for a 4 kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed sensorless broken bars detection technique