000170022 001__ 170022
000170022 005__ 20260316092629.0
000170022 0247_ $$2doi$$a10.1109/OJIES.2026.3663897
000170022 0248_ $$2sideral$$a148551
000170022 037__ $$aART-2026-148551
000170022 041__ $$aeng
000170022 100__ $$0(orcid)0009-0002-3255-5775$$aLahuerta, Oscar$$uUniversidad de Zaragoza
000170022 245__ $$aHybrid-timescale physics-informed neural network for electrical equivalent impedance identification in induction heating systems
000170022 260__ $$c2026
000170022 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170022 5203_ $$aThis 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, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.
000170022 536__ $$9info:eu-repo/grantAgreement/EUR/AEI/CPP2021-008938$$9info:eu-repo/grantAgreement/ES/DGA/T26-24$$9info:eu-repo/grantAgreement/ES/DGA/T34-24$$9info:eu-repo/grantAgreement/ES/MICIU/PDC2023-145837-I00$$9info:eu-repo/grantAgreement/ES/MICIU/PID2022-136621OB-I00
000170022 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000170022 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170022 700__ $$0(orcid)0000-0001-7901-9174$$aCarretero, Claudio$$uUniversidad de Zaragoza
000170022 700__ $$0(orcid)0000-0003-4633-4551$$aBarragan, Luis Angel
000170022 700__ $$0(orcid)0000-0002-0795-8743$$aNavarro, Denis$$uUniversidad de Zaragoza
000170022 700__ $$0(orcid)0000-0001-7207-5536$$aAcero, Jesus$$uUniversidad de Zaragoza
000170022 7102_ $$12002$$2385$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Física Aplicada
000170022 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000170022 773__ $$g7 (2026), 382-392$$pIEEE open j. ind. electron. soc.$$tIEEE Open Journal of the Industrial Electronics Society$$x2644-1284
000170022 8564_ $$s3235746$$uhttps://zaguan.unizar.es/record/170022/files/texto_completo.pdf$$yVersión publicada
000170022 8564_ $$s2391520$$uhttps://zaguan.unizar.es/record/170022/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170022 909CO $$ooai:zaguan.unizar.es:170022$$particulos$$pdriver
000170022 951__ $$a2026-03-16-08:16:59
000170022 980__ $$aARTICLE