Open AccessInduction motor fault detection based on multi‐sensory control and wavelet analysis
Author(s) -
Abubakar Ukashatu,
Mekhilef Saad,
Gaeid Khalaf S.,
Mokhlis Hazlie,
Al Mashhadany Yousif I.
Publication year - 2020
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2020.0030
Subject(s) - control theory (sociology) , stator , robustness (evolution) , induction motor , vector control , fault detection and isolation , fault tolerance , fault (geology) , control engineering , computer science , engineering , voltage , control (management) , artificial intelligence , actuator , mechanical engineering , distributed computing , biochemistry , chemistry , electrical engineering , seismology , gene , geology
Detection and fault‐tolerant control (FTC) of faults in the early stage is desirable in improving efficiency. An implementation strategy is proposed for the individual controllers that work collectively in induction motor (IM) drive by interswitching from one form of a control strategy to another. The interswitching occurs between voltage by frequency (V/f) open‐loop control, closed‐loop (V/f) control, sensorless vector control and sensor vector control. Optimal performance capabilities are attained with vector control, whereas V/f is a setup that is affordable but with increased speed. In this study, the faults are open and short circuits winding faults, speed sensor failures and stator winding faults. When the severity of the fault is high, an embedded protection entity interrupts the motor. Daubechies 10 wavelet is used due to its significant vanishing moments compared to the other types of Daubechies as fault index with the stator current of 1 kW IM. A novel enhanced model reference adaptive system is employed for sensorless vector control to assess the motor speed. Both the simulation and experiment (using the F28335 DSP controller) indicate that the framework is effective in detecting the fault, ensuring the robustness of the FTC scheme and proving the effectiveness of the proposed algorithm.