Open AccessNon‐invasive load monitoring of induction motor drives using magnetic flux sensors
Author(s) -
Liu Zheng,
Tian Guiyun,
Cao Wenping,
Dai Xuewu,
Shaw Brian,
Lambert Robert
Publication year - 2017
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2016.0304
Subject(s) - spectrogram , stator , induction motor , rotor (electric) , sensitivity (control systems) , magnetic flux , control theory (sociology) , engineering , magnetic flux leakage , transient (computer programming) , computer science , acoustics , voltage , electronic engineering , magnetic field , physics , magnet , electrical engineering , artificial intelligence , control (management) , quantum mechanics , operating system
Existing load monitoring methods for induction machines are generally effective, but suffer from sensitivity problems at low speeds and non‐linearity problems at high supply frequencies. This study proposes a new non‐invasive load monitoring method based on giant magnetoresistance flux sensors to trace stray flux leaking from induction motors. Finite element analysis is applied to analyse stray flux features of test machines. Contrary to the conventional methods of measuring stator and/or rotator rotor voltage and current, the proposed method measures the dynamic magnetic field at specific locations and provides time‐spectrum features (e.g. spectrograms), response time load and stator/rotor characteristics. Three induction motors with different starting loading profiles are tested at two separate test benches and their results are analysed in the time‐frequency domain. Their steady features and dynamic load response time through spectrograms under variable loads are extracted to correlate with load variations based on spectrogram information. In addition, the transient stray flux spectrogram and time information are more effective for load monitoring than steady state information from numerical and experimental studies. The proposed method is proven to be a low‐cost and non‐invasive method for induction machine load monitoring.