169087 20260218135106.0 doi 10.1016/j.engappai.2026.114093 sideral 148186 ART-2026-148186 eng Herrero Jaraba, Elías Universidad de Zaragoza (orcid)0000-0002-9582-8964 Hybrid Inception-Transformer model for signals classification: The case of electrical faults in power transformers 2026 This 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. Access copy available to the general public Unrestricted info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Martínez Carrasco, Eduardo Prada Hurtado, Anibal Antonio Villen Martínez, María Teresa Rios Gómez, Guillermo Hernando Polo, David Buldain Pérez, Julio David (orcid)0000-0003-3431-5863 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 168 (2026), 114093 [17 pp.] Eng. appl. artif. intell. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 0952-1976 3756801 http://zaguan.unizar.es/record/169087/files/texto_completo.pdf Versión publicada 2646231 http://zaguan.unizar.es/record/169087/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:169087 articulos driver 2026-02-18-12:24:03 ARTICLE