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The purpose of this paper is to suggest a procedure which makes it possible to reduce the radial vibrations of doubly salient switched reluctance motors (SRMs).

An analytical method for the SRM radial vibration determination is first described. It is then extended to the active vibration reduction. An auxiliary winding equips the stator. The paper explains how the corresponding currents have to be adjusted to achieve a simple and robust control, with a special emphasis about the compatibility of the main and auxiliary supplies and about the reduction control principle. At last, an example of drastic noise reduction is presented.

The proposed method makes possible to define the theoretical vibration spectrum of SRM and thus it gives the major components to be reduced. The feasibility of automating the principle of active reduction is shown. The process of active reduction shows that a vibration component can be diminished by over 90 percent.

The active reduction is applied for reducing one component of the vibration spectrum. Future developments will focus on the simultaneous reduction of several components of vibration spectrum.

The method offers an automated process to reduce considerably the component of highest amplitude in the vibration spectrum.

This paper aims to propose a high‐frequency (HF) model able to compute the flux density in the vicinity of the laminated stator core of an AC machine.

Experiments form the main approach. Analytical results previously obtained with a simplified rectangular laminated structure are confirmed with a standard cylindrical magnetic core.

Three frequency domains are defined, depending on the skin depth relative to the thickness of the magnetic sheets. A methodological approach is proposed for each domain. For higher frequencies, the magnetic core can be considered as transparent for external field computation.

The HF model is valid for skin depths much lower than the thickness of the magnetic sheets.

The proposed HF model provides a link between the weak field measured in the natural void existing between the stator core and the housing of large electrical machines. With such a link, it is possible to develop a new monitoring system able to detect and to localize the partial discharges in the stator winding of a large machine.

The low‐frequency limit of the model has been measured. It corresponds to a ratio of 1/40 between the skin depth and the magnetic sheet thickness. Therefore this model offers a new perspective for maintenance applications.

The purpose of this paper is to find a more performing and automated procedure for linking an identification algorithm implemented in a general‐purpose environment with a commercial finite‐element code for magnetic field analysis. In particular, the use of a multiprocessor computer makes it possible to perform parallel computations keeping the calculation time reasonably low.

The method is applied to identify the B‐H curve of anisotropic magnetic laminations in the direction normal to the sheet surface. In total, three different optimization methods have been applied. First an evolution strategy algorithm for solving the identification problem was used; then genetic algorithm (GA) was applied. The results obtained using different methods were compared and discussed. The computation time is reduced by adjusting the refinement of the FEM mesh.

The key point has been the use of a derivative‐free and global‐search oriented algorithm. Even if a starting point far from the solution is chosen, a suitably large initial value of the search radius makes the convergence possible. The effect of the historical parameter of the minimization algorithm on convergence has also been investigated.

The main new idea presented in this paper is equipping a GA‐based identification procedure with an additional objective function describing the sensitivity of the flux density against a small perturbation in parameters. This approach gives a multiple objective problem which introduces possibility of choosing a compromise solution among many optimal solutions instead of only one, as in classical GA optimization algorithm. The paper is mainly addressed to readers interested in the efficient use of GA‐based identification.

The purpose of this study is to explore the effect of the demagnetizing field in the Epstein characterization of grain-oriented electrical steels through a finite element method (FEM) simulations.

A 3D finite element simulation has been realized to represent the parallel and X-stacking configurations in the Epstein frame. The numerical results have been compared with experimental measures.

In a parallel configuration, the measured induction is actually the one in the material, whereas the resulting magnetic field differs from the applied one (in magnitude and angle) due to the shape anisotropy (demagnetizing field). In X-stacking configuration, the resulting magnetic field is close to the applied magnetic field (and then the supposed excitation field in the Epstein frame), whereas the magnetic induction has deviated from the axis of the strips.

Both stacking configurations (parallel and cross) of the Epstein frame are analyzed by three-dimensional finite element simulation.