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Detection of faults in the asynchronous machine by the use of smartmaterials

Regaz A., Zegnini B., Mahi D., Boukezzi L.

Diagnostyka

2018

Vol. 19, No. 3

43--54

This paper aimed with diagnosis of defects in asynchronous machine. The used method is based on the exploitation of the behavioural laws of magnetostrictive and piezoelectric materials, under harmonic regime, used in the construction of the asynchronous machine. Piezoelectric sensors are used both in the stator and in the rotor. The used one in the stator serves to detect the rupture at the stator windings and the one used in the rotor helps us to detect the rupture of the rotor bars. The produced electric potential in the terminals of piezoelectric sensor is due to the deformation generated by magnetic induction under the effect of magnetostriction. A finite element method (FEM) is selected to be the resolution numerical method. This method is used for modelling the asynchronous machine under anomaly conditions and non-load.

A new approach using Least Squares Support Vector Machines (LS-SVM) to predict Furan in power transformers

Mahdjoubi A., Zegnini B., Belkheiri M.

Przegląd Elektrotechniczny

2014

R. 90, nr 2

142--145

LS-SVM present recently more efficiency in different industrial applications like medicine, engineering and power systems. This paper describes a methodology that was developed for the prediction of Furan in power transformers. The methodology uses as input variables such as the dissolved gases (CO and CO2). The approach presents the advantage that can reduce the time vs. laboratory tests. The validity of the approach was examined by testing several power transformers. LS-SVM gives a good estimation of results which are validated by experimental tests.

W artykule opisano metodologię prognozowania obecności Furanu w transformatorach energetycznych. Na wejściu podawane są takie parametry jak ilość rozpuszczonych gazów CO i CO2. Metoda opiera się na wykorzystaniu algorytmów LS-SVM.