Abstract
Fault diagnosis in power transmission systems requires the simultaneous identification of fault type and location from high-dimensional, multichannel, and temporally evolving measurements. Conventional deep learning approaches may struggle to jointly capture localized transient patterns, global temporal dependencies, and spatially distributed fault signatures while providing interpretable diagnostic information. To address these challenges, a Multi-Resolution Perceiver-Based Residual Attention (MRP-RA) framework is proposed, that combines temporal convolutional feature extraction with a learnable frequency-aware projection branch, Perceiver-based latent modeling, residual spatio-temporal attention, and coupled multi-task learning for fault detection, classification, and localization. The framework processes 256-step windows of 42-channel measurements from a simulated IEEE 5-bus transmission system, where the temporal and frequency-aware representations are fused before being compressed into a latent representation using 32 learnable tokens. Residual temporal and channel attention mechanisms are employed to identify salient temporal regions and feature channels associated with fault signatures, while the multi-task objective jointly optimizes anomaly detection, fault-type classification, and fault localization. Experimental results show that MRP-RA achieves 96.47% fault-type classification accuracy and 97.64% fault-location accuracy across multiple random seeds, while achieving 3.5 × higher inference throughput than the Transformer baseline under the evaluated configuration. These results demonstrate that the proposed combination of multi-resolution feature extraction, latent global modeling, and residual attention can provide accurate joint fault diagnosis with reduced attention-related computational requirements and interpretable attention-based diagnostic insights. The findings support MRP-RA as a promising approach for explainable fault diagnosis in simulated power transmission systems, while further validation across different grid topologies, operating conditions, and real-world measurements is required to establish broader generalizability.