Open AccessOnline thermal parameter identification for permanent magnet synchronous machines
Author(s) -
Xiao Shuai,
Griffo Antonio
Publication year - 2020
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2020.0119
Subject(s) - stator , control theory (sociology) , rotor (electric) , magnet , kalman filter , thermal , extended kalman filter , pulse width modulation , estimation theory , computer science , synchronous motor , identification (biology) , temperature measurement , control engineering , engineering , mechanical engineering , algorithm , physics , electrical engineering , artificial intelligence , voltage , botany , control (management) , biology , quantum mechanics , meteorology
Temperature monitoring of permanent magnet synchronous machines (PMSMs) is of great importance because high temperatures can significantly shorten the lifetimes of motor components. Accurate temperature predictions can be achieved using reduced‐order lumped parameter thermal networks (LPTNs) with accurate thermal parameters. In this study, an online estimation method based on the recursive Kalman filter algorithm is introduced for online identification of the thermal resistances in a three‐node LPTN representing motor stator iron, stator winding and permanent magnet. The identification procedure requires a rotor temperature measurement, which is provided by an accurate pulse‐width modulation‐based estimation method. The proposed methodology is experimentally validated and applied to real‐time fault detection of the motor cooling system.