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000169087 005__ 20260218135106.0
000169087 0247_ $$2doi$$a10.1016/j.engappai.2026.114093
000169087 0248_ $$2sideral$$a148186
000169087 037__ $$aART-2026-148186
000169087 041__ $$aeng
000169087 100__ $$0(orcid)0000-0002-9582-8964$$aHerrero Jaraba, Elías$$uUniversidad de Zaragoza
000169087 245__ $$aHybrid Inception-Transformer model for signals classification: The case of electrical faults in power transformers
000169087 260__ $$c2026
000169087 5060_ $$aAccess copy available to the general public$$fUnrestricted
000169087 5203_ $$aThis paper presents a hybrid deep learning model for fault detection in power transformers, addressing the limitations of conventional protection schemes under transient operating conditions. The proposed model, TransInception, integrates InceptionTime for efficient feature extraction in multivariate time series and Gated Transformer for capturing dependencies between variables. The architecture is modified by replacing the original gating mechanism with a linear double-layer output and removing a bottleneck layer responsible for handling temporal dependencies. The dataset used for training and testing was generated in a real-time digital simulation (RTDS) environment, consisting of an external grid, a delta-wye transformer, and a dynamic load. After training, the hybrid deep learning model was validated in a test grid specifically designed for this stage, where a parallel transformer configuration was implemented. This validation allowed for the evaluation of its performance in classifying internal, external, and no-fault conditions, as well as assessing cases of current transformer saturation. Additionally, sympathetic inrush conditions were studied to analyse the model’s response to interactions between power transformers. As future work, efforts will focus on improving the model’s adaptability to transient conditions and optimising its computational efficiency for deployment in
substation protection systems.
000169087 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000169087 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000169087 700__ $$aMartínez Carrasco, Eduardo
000169087 700__ $$aPrada Hurtado, Anibal Antonio
000169087 700__ $$aVillen Martínez, María Teresa
000169087 700__ $$aRios Gómez, Guillermo
000169087 700__ $$aHernando Polo, David
000169087 700__ $$0(orcid)0000-0003-3431-5863$$aBuldain Pérez, Julio David
000169087 7102_ $$15008$$2785$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Tecnología Electrónica
000169087 773__ $$g168 (2026), 114093 [17 pp.]$$pEng. appl. artif. intell.$$tENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE$$x0952-1976
000169087 8564_ $$s3756801$$uhttps://zaguan.unizar.es/record/169087/files/texto_completo.pdf$$yVersión publicada
000169087 8564_ $$s2646231$$uhttps://zaguan.unizar.es/record/169087/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000169087 909CO $$ooai:zaguan.unizar.es:169087$$particulos$$pdriver
000169087 951__ $$a2026-02-18-12:24:03
000169087 980__ $$aARTICLE