Hybrid Inception-Transformer model for signals classification: The case of electrical faults in power transformers

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. • Detection of electrical faults in power transformers with advanced machine learning techniques. • Fast and reliable method for detecting and classifying faults in electrical equipment. • Advanced monitoring and protection of power systems (power transformer). • Database of different electrical faults in an electrical power transformer.