journal article·2024-08Open access

Magnetic behavior of a laminated magnetic core in the presence of interlaminar faults: A simulation method based on fractional operators

B. Ducharne, H. Hamzehbahmani, R.V. Sabariego, Y. Gao

Abstract

Stacks of grain-oriented silicon steel (GO FeSi) laminations play a crucial role as magnetic cores of power transformers. These cores undergo degradation over time due to corrosion, thermal cycles, etc. Geometrical abnormalities and residual stress from manufacturing processes exacerbate these degradation processes. Edge burrs can form around cut edges and lead to InterLaminar Faults (ILFs). In a recent work, we described an innovative method for simulating dynamical GO FeSi lamination hysteresis cycles. This method can be applied without any change to a stack of electrically isolated laminations, like in a magnetic core. It is especially easy when the working conditions impose a homogeneous behavior (B-imposed conditions). The simulation technique combines the resolution of the magnetic diffusion equation and a fractional differential equation as material law, yielding excellent simulation results across a broad frequency range with only two parameters accounting for the dynamic contribution. This new article outlines the successful extension of this simulation method to consider ILFs and predict their impact on the performances. For this, lamination stacks were initially simulated under full short-circuit conditions. Then, we used linear combinations between responses from these stacks and flawless ones. The simulation successfully reproduced the experimental data obtained for one or three aligned ILFs on several conditions. Then it was used to predict the behavior of additional aligned ILFs and/or different numbers of laminations in the simulated stack.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Magnetic Properties and Applications · Microstructure and Mechanical Properties of Steels · Induction Heating and Inverter Technology

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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