A computationally efficient and robust neural network-based model to reproduce the hysteresis phenomenon for soft ferromagnetic alloys is here presented, as well as a dedicated procedure to generate a suitable training set from a minimal set of experimental data. Firstly, an accurate experimental verification has been performed for a commercial NGO electrical steel, measuring a family of hysteresis loops under sinusoidal and non-sinusoidal magnetic induction waveforms. The Preisach model of hysteresis, identified with the sinusoidal loops, has been exploited to generate a wider data set, which consists of a family of first-order reversal curves (FORCs), suitable to train the neural network. Then, a neural network-based hysteresis model, with the capability to reproduce the eventual presence of sub-loops, has been developed. The two simulation approaches have been validated taking into account the other experimental data, which consist of a family of hysteresis loops measured under different types of magnetic induction waveforms. The comparison between the Preisach model and the neural network-based model also covers the simulation of the waveforms found in magnetic systems supplied by pulse-width modulated (PWM) signals. The substantial agreement found indicates that the neural network model can replicate the behaviour of the Preisach model with a considerable advantage in terms of computational cost and memory allocation. In addition, the possibility to be quickly inverted makes the proposed method suitable for matching with FEM solvers.

Quondam Antonio, S., Riganti Fulginei, F., Laudani, A., Faba, A., Cardelli, E. (2021). An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms. JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, 528, 167735 [10.1016/j.jmmm.2021.167735].

An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms

Riganti Fulginei F.;Laudani A.;
2021-01-01

Abstract

A computationally efficient and robust neural network-based model to reproduce the hysteresis phenomenon for soft ferromagnetic alloys is here presented, as well as a dedicated procedure to generate a suitable training set from a minimal set of experimental data. Firstly, an accurate experimental verification has been performed for a commercial NGO electrical steel, measuring a family of hysteresis loops under sinusoidal and non-sinusoidal magnetic induction waveforms. The Preisach model of hysteresis, identified with the sinusoidal loops, has been exploited to generate a wider data set, which consists of a family of first-order reversal curves (FORCs), suitable to train the neural network. Then, a neural network-based hysteresis model, with the capability to reproduce the eventual presence of sub-loops, has been developed. The two simulation approaches have been validated taking into account the other experimental data, which consist of a family of hysteresis loops measured under different types of magnetic induction waveforms. The comparison between the Preisach model and the neural network-based model also covers the simulation of the waveforms found in magnetic systems supplied by pulse-width modulated (PWM) signals. The substantial agreement found indicates that the neural network model can replicate the behaviour of the Preisach model with a considerable advantage in terms of computational cost and memory allocation. In addition, the possibility to be quickly inverted makes the proposed method suitable for matching with FEM solvers.
Quondam Antonio, S., Riganti Fulginei, F., Laudani, A., Faba, A., Cardelli, E. (2021). An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms. JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS, 528, 167735 [10.1016/j.jmmm.2021.167735].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/380710
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