Hybrid-Timescale Physics-Informed Neural Network for Electrical Equivalent Impedance Identification in Induction Heating Systems

This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specific...

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Главные авторы:	Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan, Denis Navarro, Jesus Acero
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IEEE 2026-01-01
Серии:	IEEE Open Journal of the Industrial Electronics Society
Предметы:	Home appliances induction heating (IH) physics-informed machine learning system identification
Online-ссылка:	https://ieeexplore.ieee.org/document/11393584/
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