A Multi-Scale Time Adaptive Fusion Network for Transformer Fault Diagnosis

Transformer fault diagnosis is crucial for the safe operation of power systems, enabling quick and accurate fault type identification. However, traditional methods struggle with extracting multi-scale temporal features and high-order feature representations, limiting their ability to handle complex dynamic data patterns. To address this, this paper proposes a multi-scale temporal adaptive fusion network (MSTAFN). The MSTAFN model first generates a time position vector through a temporal information encoding (TIE) module, capturing multi-scale temporal features. The adaptive high-order hybrid network (AHOHN) module then fuses multi-scale temporal data with transformer features using a hybrid attention mechanism, extracting temporal variation patterns. To enhance high-order feature representation, the high-order feature extraction (HOFE) module introduces nonlinear activation and higher-order operations to capture complex relationships between features. The adaptive feature reconstruction (AFR) module dynamically adjusts the feature fusion ratio, optimizing information integration. Finally, the multi-scale temporal fusion (MSTF) module balances the fusion of multi-scale temporal features and global dependencies, adapting to different tasks and data distributions. Extensive experiments on publicly available datasets demonstrate that the MSTAFN model outperforms comparison models across multiple evaluation metrics, proving its effectiveness and superiority in transformer fault diagnosis.