The magnetostriction of grain-oriented electrical steels is important for evaluating the core vibrations of operational power transformers. However, magnetostriction is regarded as the square of magnetization in most studies, where influencing factors such as stress and magnetic saturation effects are disregarded. In this study, a two-step magnetostriction-prediction technique based on a physics-informed neural network (PINN) is proposed. First, the physical model of magnetostriction is deduced from the classical Jiles-Atherton model by introducing an elastic potential energy term. Second, a PINN-based model is used to obtain the resulting magnetostriction by adding the residual of the physical equation. The magnetic parameters and magnetostriction of the test specimens are measured experimentally under different excitation voltages and tensile stresses. Results show that magnetostriction is a highly stress-dependent phenomenon. As the tensile stress increases, the elastic potential energy gradually becomes the dominant factor affecting magnetostriction, thereby resulting in a significant amplitude shift in the frequency domain. The performance prediction of the PINN is significantly better than that of artificial neural networks, particularly for unknown samples with slight voltage variations.

中文翻译：

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基于物理信息的神经网络磁致伸缩晶粒取向电工钢模型

晶粒取向电工钢的磁致伸缩对于评估运行电力变压器的铁芯振动非常重要。然而，大多数研究将磁致伸缩视为磁化强度的平方，忽略了应力和磁饱和效应等影响因素。在这项研究中，提出了一种基于物理信息神经网络（PINN）的两步磁致伸缩预测技术。首先，通过引入弹性势能项，从经典的 Jiles-Atherton 模型推导出磁致伸缩的物理模型。其次，使用基于 PINN 的模型通过添加物理方程的残差来获得最终的磁致伸缩。在不同的激励电压和拉应力下，通过实验测量了测试样品的磁参数和磁致伸缩。结果表明，磁致伸缩是一种高度依赖于应力的现象。随着拉应力的增加，弹性势能逐渐成为影响磁致伸缩的主导因素，从而导致频域中出现显着的振幅偏移。 PINN 的性能预测明显优于人工神经网络，特别是对于电压变化较小的未知样本。