Open AccessStator winding inter‐turn short‐circuit detection in induction motors by parameter identification
Author(s) -
Abdallah Hamoudi,
Benatman Kouadri
Publication year - 2017
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.2016.0432
Subject(s) - stator , fault detection and isolation , control theory (sociology) , electromagnetic coil , induction motor , sensitivity (control systems) , line (geometry) , estimation theory , computer science , fault (geology) , algorithm , engineering , electronic engineering , mathematics , actuator , artificial intelligence , voltage , control (management) , electrical engineering , mechanical engineering , geometry , seismology , geology
The estimation of induction motor (IM) parameters is essential for monitoring, diagnosis and control. Within the framework of the diagnosis of the stator windings faults, this study presents two algorithms for off‐line and on‐line inter‐turn short‐circuits detection by parameter identification. The first approach combines trust‐region and Broyden–Fletcher–Goldfard–Shano methods to exploit both of their advantages. This algorithm is used for off‐line minimisation of the objective function represented by a quadratic‐criterion. The second algorithm, based on the moving horizon estimation, combines off‐line measurement and on‐line parameter estimation with high sampling time to monitor in real‐time the stator inter‐turn faults in IMs. Since it requires a mathematical model suited for fault modelling, a faulty IM model is presented. Due to the parameters values of this model present magnitude order very different, the normalisation of these parameters is proposed to obtain the sensitivity functions with the same magnitude order. The estimation results, which used simulated as well as experimental data, are presented to show the effectiveness and the advantage of the proposed algorithms for stator inter‐turn short‐circuits faults detection in IMs.