Open AccessSensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives
Author(s) -
Zhang Xinan,
Foo Gilbert,
Don Vilathgamuwa Mahinda,
Tseng King Jet,
Bhangu Bikramjit Singh,
Gajanayake Chandana
Publication year - 2013
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2012.0308
Subject(s) - control theory (sociology) , fault detection and isolation , kalman filter , control reconfiguration , current sensor , computer science , fault (geology) , induction motor , position sensor , reliability (semiconductor) , control engineering , voltage , engineering , power (physics) , control (management) , actuator , embedded system , electrical engineering , artificial intelligence , physics , quantum mechanics , rotor (electric) , seismology , geology
Induction motors (IMs) have been extensively used in industrial applications because of their inexpensiveness, ruggedness and reliability. Generally, to improve the performance of IM drives, one position sensor, one DC‐link voltage sensor and at least two AC current sensors are necessary. However, failure of any of these sensors can cause degraded system performance or even instability. Consequently, it is very important to develop a sensor fault resilient control system for IMs drives so that continuous and normal operation is maintained even in cases of sensor faults. This study proposes a compact and robust sensor fault detection, isolation and system reconfiguration algorithm based on extended Kalman filter and reduced number of adaptive observers. A comprehensive set of experimental results are provided to verify the effectiveness of the proposed algorithm.