Detection of Stator Fault in Synchronous Reluctance Machines Using Shallow Neural Networks

Siwan Narayan (1) , Rahul R. Kumar , Giansalvo Cirrincione (2) , Maurizio Cirrincione (3)

1 USP - University of the South Pacific
2 LTI - Laboratoire des technologies innovantes - UR UPJV 3899
3 USP - University of the South Pacific

Fault detection in electrical drives can be really challenging, especially when the input data is collected from an operational electrical machine. In order to prevent machine damages and downtimes, it is really important to detect pre-fault conditions. This paper presents the detection of stator inter-turn fault for Synchronous Reluctance Motor (SynRM) with a severity as low as 1.3%. After the transformation of the three-phase currents using Extended Park Vector (EPV) approach, the temporal features were calculated. Thereafter, the geometry of the features has been studied by using the Principal Component Analysis (PCA) and the Curvilinear Component Analysis (CCA) to estimate the best intrinsic dimensionality and extract the most significant features. Finally, a variety of classifiers have been trained with this feature-set (FS) and the shallow neural network has proved to give the best performance.

Domaines: Sciences de l'ingénieur [physics]

Siwan Narayan, Rahul R. Kumar, Giansalvo Cirrincione, Maurizio Cirrincione. Detection of Stator Fault in Synchronous Reluctance Machines Using Shallow Neural Networks. 2021 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), Oct 2021, Vancouver, Canada. pp.1347-1352, ⟨10.1109/ECCE47101.2021.9595518⟩. ⟨hal-03716205⟩