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The specific modulation methods are used to control different kind of single-phase, as well as three-phase, inverters to ensure flexibility and high quality of the output waveform. This paper aims to present a combination of two classical methods, namely, pulse width modulation method and direct digital synthesis modulation method.

The total harmonic distortion of output waveforms of single-phase inverter based on elaborated modulation method has been determined by means of fast Fourier transform analysis. Tests have been carried out by using standard low-frequency application and also a wireless resonant energy link system.

Applying appropriate timer parameters of microcontroller enables to obtain a waveform for given output parameters (amplitude, frequency, frequency modulation index, etc.). The only limitation is the computing power of a microcontroller.

The elaborated method can be successfully used in both low- and high-frequency application ensuring high level of output waveform quality. Additional signal generators and the control of amplitude modulation ratio are no longer indispensable, what simplify immensely a control system.

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.

The diagnostics of electrical machines is a very important task. The paper seeks to present a study and analysis of stator winding asymmetry in induction motors. The purpose of this paper is presentation of coupling two numerical techniques, a finite element analysis and an artificial neural network, in diagnostics of electrical machines.

A finite element method (FEM) analysis and time‐stepping are applied for the study of IM with stator winding asymmetry. One of the asymmetry symptoms is an axial flux. In order to determine the level of winding asymmetry a generalized regression neural network has been considered. The result of FFT analysis of axial flux and electromagnetic torque was the input vector to artificial neural network. The output vector is the level of asymmetry. The algorithms are tested using a set data obtained from numerical simulation. The emphasis of this structure is on accurate approximation of the value of the stator winding asymmetry.

The axial flux, as the symptom of stator winding asymmetry, can contribute to better detection of the asymmetry in stator winding.

It is argued that the proposed method based on axial flux and electromagnetic torque is capable of performing detection of the asymmetry in stator winding. The generalized regression neural network can be used in health monitoring system as an inference module.