000098262 001__ 98262
000098262 005__ 20231116120809.0
000098262 0247_ $$2doi$$a10.1109/ACCESS.2019.2960109
000098262 0248_ $$2sideral$$a121925
000098262 037__ $$aART-2019-121925
000098262 041__ $$aeng
000098262 100__ $$0(orcid)0000-0002-1284-9007$$aLucia, O.$$uUniversidad de Zaragoza
000098262 245__ $$aDeep Learning-Based Magnetic Coupling Detection for Advanced Induction Heating Appliances
000098262 260__ $$c2019
000098262 5060_ $$aAccess copy available to the general public$$fUnrestricted
000098262 5203_ $$aInduction heating has become the reference technology for domestic heating applications due to its benefits in terms of performance, efficiency and safety, among others. In this context, recent design trends aim at providing highly flexible cooking surfaces composed of multi-coil structures. As in many other wireless power transfer systems, one of the main challenges to face is the proper detection of the magnetic coupling with the induction heating load in order to provide improved thermal performance and safe power electronic converter operation. This is specially challenging due to the high variability in the materials used in cookware as well as the random pot placement in flexible induction heating appliances. This paper proposes the use of deep learning techniques in order to provide accurate area overlap estimation regardless of the used pot and its position. An experimental test-bench composed of a complete power converter, multi-coil system and real-Time measurement system has been implemented and used in this study to characterize the parameter variation with overlapped area. Convolutional neural networks are then proposed as an effective method to estimate the covered area, and several implementations are studied and compared according to their computational cost and accuracy. As a conclusion, the presented deep learning-based technique is proposed as an effective tool to estimate the magnetic coupling between the coil and the induction heating load in advanced induction heating appliances.
000098262 536__ $$9info:eu-repo/grantAgreement/ES/DGA/FSE$$9info:eu-repo/grantAgreement/ES/MECD-DGA-FSE/FPU17-01442$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/RTC-2017-5965-6$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2016-78358-R
000098262 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000098262 590__ $$a3.745$$b2019
000098262 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b35 / 155 = 0.226$$c2019$$dQ1$$eT1
000098262 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b61 / 265 = 0.23$$c2019$$dQ1$$eT1
000098262 591__ $$aTELECOMMUNICATIONS$$b26 / 89 = 0.292$$c2019$$dQ2$$eT1
000098262 592__ $$a0.775$$b2019
000098262 593__ $$aEngineering (miscellaneous)$$c2019$$dQ1
000098262 593__ $$aComputer Science (miscellaneous)$$c2019$$dQ1
000098262 593__ $$aMaterials Science (miscellaneous)$$c2019$$dQ2
000098262 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000098262 700__ $$0(orcid)0000-0002-0795-8743$$aNavarro, D.$$uUniversidad de Zaragoza
000098262 700__ $$0(orcid)0000-0003-4886-9461$$aGuillen, P.$$uUniversidad de Zaragoza
000098262 700__ $$0(orcid)0000-0001-8399-4650$$aSarnago, H.$$uUniversidad de Zaragoza
000098262 700__ $$aLucia, S.
000098262 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000098262 773__ $$g7 (2019), 181668-181677$$pIEEE Access$$tIEEE Access$$x2169-3536
000098262 8564_ $$s1342944$$uhttps://zaguan.unizar.es/record/98262/files/texto_completo.pdf$$yVersión publicada
000098262 8564_ $$s2567617$$uhttps://zaguan.unizar.es/record/98262/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000098262 909CO $$ooai:zaguan.unizar.es:98262$$particulos$$pdriver
000098262 951__ $$a2023-11-16-12:01:02
000098262 980__ $$aARTICLE