@inproceedings{e943447ae545410d8ecf2233e21e181c,
title = "Permanent magnet synchronous machines inter-turn short circuit fault detection by means of model-based residual analysis",
abstract = "Short circuit faults in the stator winding are a common failure mode in Permanent Magnet Synchronous Machines (PMSM). Model-based fault detection has a potential benefit of being less sensitive to transient conditions compared to frequency-domain based methods for fault detection. However, accurate models of the machine being monitored are required in order to produce accurate fault detection whilst avoiding false alarms. This paper presents a model-based fault detection method based on the analysis of the residual between the measured currents and those predicted by an accurate real-time model of the machine. The model is nonlinear and based in pre-calculated flux linkages look up tables obtained through magnetostatic finite element analysis (FEA), accounting for magnetic saturation and spatial harmonics. The use of a residual provides a mean to amplify frequency components caused by a fault. Computer simulations, real-time Hardware-in-the-Loop (HIL) methods and experimental validations are provided.",
keywords = "Fault detection, Hardware-in-the-Loop, Inter-turn fault, Modelling, Permanent Magnet Synchronous Machines",
author = "Fernando Alvarez-Gonzalez and Antonio Griffo and Bo Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 ; Conference date: 20-10-2018 Through 23-10-2018",
year = "2018",
month = dec,
day = "26",
doi = "10.1109/IECON.2018.8591661",
language = "English",
series = "Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "647--652",
booktitle = "Proceedings",
address = "United States",
}
