Diagnostics of electrical machines is a very important task. The purpose of this paper is the presentation of coupling three numerical techniques, a finite element analysis, a signal analysis and an artificial neural network, in diagnostics of electrical machines. The study focused on detection of a time-varying inter-turn short-circuit in a stator winding of induction motor.
A finite element method is widely used for the calculation of phase current waveforms of induction machines. In the presented results, a time-varying inter-turn short-circuit of stator winding has been taken into account in the elaborated field-circuit model of machine. One of the time-varying short-circuit symptoms is a time-varying resistance of shorted circuit and consequently the waveform of phase current. A general regression neural network (GRNN) has been elaborated to find a number of shorted turns on the basis of fast Fourier transform (FFT) of phase current. The input vector of GRNN has been built on the basis of the FFT of phase current waveform. The output vector has been built upon the values of resistance of shorted circuit for respective values of shorted turns. The performance of the GRNN was compared with that of the multilayer perceptron neural network.
The GRNN can contribute to better detection of the time-varying inter-turn short-circuit in stator winding than the multilayer perceptron neural network.
It is argued that the proposed method based on FFT of phase current and GRNN is capable to detect a time-varying inter-turn short-circuit. The GRNN can be used in a health monitoring system as an inference module.
Pietrowski, W. (2017), "Detection of time-varying inter-turn short-circuit in a squirrel cage induction machine by means of generalized regression neural network", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 36 No. 1, pp. 289-297. https://doi.org/10.1108/COMPEL-03-2016-0121Download as .RIS
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