Wojciech Pietrowski

Calculation of magnetization characteristic of a squirrel cage machine using edge element method

This paper presents the finite element method for the calculation of open‐circuit characteristic of a squirrel cage machine with saturated core. The flux linkage with the stator winding and the winding inductances have been calculated using the edge element method. The calculations show that the equivalent inductance of a balanced three‐phase no‐loaded induction machine with saturated core may be defined like a quadrature‐axis inductance in synchronous machine. The algorithm of this inductance calculation has been proposed. The equivalent inductances have been used in the calculation of electromotive force. The results obtained from numerical calculations have been compared with the measured results.

Calculation of end‐turn leakage inductances of electrical machines using the edge element method

The end‐turn leakage inductances of the armature winding of the permanent magnet motor have been calculated. In order to describe the magnetic field distribution the edge element method using vector magnetic potential has been applied. First, the formulae that describe the total self‐inductance and total mutual conductance for phase windings are presented. Three‐dimensional and two‐dimensional formulations are considered. The end‐turn leakage inductances have been obtained by comparing the results of these formulations. The symmetrical components transformation has been applied, and the self inductances and mutual inductances have been transformed into the zero‐sequence and positive‐sequence inductances. The calculations have been performed for different dimensions of the coil‐end region. The influence of the position of the boundary surfaces on the results has been investigated.

3D edge element analysis of saturation effects in a permanent magnet machine

The paper presents a method for the 3D edge element analysis of saturation effects in the classical rotating electrical machines of cylindrical structure. The edge element (EE) method using vector magnetic potential has been applied. Special attention is paid to the saturation effects in permanent magnet motors. In order to solve the non‐linear EE equations the authors propose to apply the modified Newton algorithm with block relaxation solver and Cholesky decomposition procedure for block matrices. The convergence of the algorithm is analysed. The influence of core non‐linearity on the values of electromagnetic torque and armature inductances is considered. The results for 3D and 2D models are compared.

Detection of time-varying inter-turn short-circuit in a squirrel cage induction machine by means of generalized regression neural network

Purpose 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. Design/methodology/approach 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. Findings The GRNN can contribute to better detection of the time-varying inter-turn short-circuit in stator winding than the multilayer perceptron neural network. Originality/value 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.

3D analysis of influence of stator winding asymmetry on axial flux

Purpose – 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.Design/methodology/approach – 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.Findings –...

Wavelet analysis of torque at startup of an induction machine under inter-turn short-circuit

The paper deals with a field-circuit transient analysis of a squirrel cage induction motor taking into account a inter-turn short circuit. Numerical simulations were carried out for a selected number of shorted turns. The elaborated model consists of field, circuit and motion equations. Based on the models, torque waveforms have been charted. Torque waveforms have been designated for two states of the machine - one with a rated load and the other with no-load. Special attention was paid to analysis of torque during the startup state. Due to the non-stationary of the torque waveform, the wavelet analysis was used.

Wavelet torque analysis and neural network in detection of induction motor inter-turn short-circuit

Progress in field of electrical machines diagnosis and artificial intelligence contributed to increased interest in new methods of diagnosis in field of electrical motors. The paper focused on three numerical techniques: finite element analysis, signal analysis and artificial neural networks in diagnosis of a squirrel cage induction machine under inter-turn short circuit in stator windings. A field-circuit model of machine was used to calculate waveforms of torque for a selected number of shorted turns. The obtained waveforms were analysed by wavelet transform. Results of analysis were used as input vector of artificial neural network. An output vector of artificial neural network was a number of shorted turns of stator winding. In this paper generalized regression neural network (GRNN) were compared with multi layer perceptron neural network (MLP).