ABSTRACT: In this work, the low frequency electromagnetic flux density around induction motors is studied; the main objective is to
provide safety regions for humans in the vicinity of these motors, especially in electrical vehicles, where high currents
and hence high flux density emissions are expected. A new equivalent magnetic circuit which accounts for stray magnetic fields
is developed. The analysis shows that the emission of the stray field in the radial directions depends on the permeability
of the stator body as well as the ampere turn of the stator winding. Small values of stator body permeability may result in
very high stray flux emissions at levels that may require shielding to protect passengers just above the motor. Relatively
far away from the stator (e.g., 50cm for the tested motors), the flux is normally of low level and may not represent an exposure
Electrical Engineering 04/2012; 91(1):15-21. · 0.40 Impact Factor
ABSTRACT: Three types of methods are studied to calculate the shielding efficiency of a cylindrical ferromagnetic shield in Fe–Si steel, i.e. an analytical method, a finite element method and a neural network method. The methods take into account nonlinear hysteretic behaviour in the shield. All of the methods are compared with each other and with measurements: the shielding factor is computed and measured for several shield radii, thicknesses, field amplitudes and frequencies. The mean absolute error rates are calculated for AM, FEM and NN to be 9.16%, 6.45% and 5.54%, respectively. The analytical model is very fast and accurate for materials with mild nonlinearity. In case of highly nonlinear material and a strongly non-uniform field distribution in the shield, the modeling of the nonlinear behavior in the analytical model is not accurate. The FEM is very accurate for all considered shielding configurations if the mesh density is well chosen, but its evaluation time is high. The neural network has the disadvantage that it should be trained. It is, however, a fast method with the additional advantage that it can be used without any knowledge of the shield geometry and material properties if the training is based on measurements.
Simulation Modelling Practice and Theory. 01/2010;
Simulation Modelling Practice and Theory. 01/2009; 17:1267-1275.
ABSTRACT: The attenuation of extremely low-frequency magnetic fields is important in reducing electromagnetic interference on electric and electronic equipment. In this paper, an innovative method is presented for shielded magnetic field level estimation at power frequencies by a neural network (NN) technique which uses experimental data. The utilized NN is applied to cylindrical shields (transformer-grade iron, copper, and aluminum) in various shield arrangements. Using the developed NN model, the mitigated magnetic field of multilayered shields is measured and evaluated to predict the magnetic field at any distance apart from the magnetic source. The NN, which is based on a feed-forward neural network (FNN), is trained with scaled conjugate gradient, gradient descent with momentum and adaptive learning back propagation, and Levenberg–Marquardt algorithms to compute the shielded magnetic field. Results have shown that the developed FNN trained with the Levenberg–Marquardt algorithm is better than the other training algorithms in predicting the shielded magnetic field value accurately even in the presence of various shield arrangements.
Expert Systems with Applications.