MH-WMG: A Multi-Head Wavelet-Based MobileNet with Gated Linear Attention for Power Grid Fault Diagnosis

Artificial intelligence is increasingly embedded in power systems to boost efficiency, reliability, and automation. This study introduces an end-to-end, AI-driven fault-diagnosis pipeline built around a Multi-Head Wavelet-based MobileNet with Gated Linear Attention (MH-WMG). The network takes time-series signals converted into images as input and branches into three heads that, respectively, localize the fault area, classify the fault type, and predict the distance bin for all short-circuit faults. Evaluation employs the canonical Kundur two-area four-machine system, partitioned into six regions, twelve fault scenarios (including normal operation), and twelve predefined distance bins. MH-WMG achieves high performance: perfect accuracy, precision, recall, and F1 (1.00) for fault-area detection; strong fault-type identification (accuracy = 0.9604, precision = 0.9625, recall = 0.9604, and F1 = 0.9601); and robust distance-bin prediction (accuracy = 0.8679, precision = 0.8725, recall = 0.8679, and F1 = 0.8690). The model is compact and fast (2.33 M parameters, 44.14 ms latency, 22.66 images/s) and outperforms baselines in both accuracy and efficiency. The pipeline decisively outperforms conventional time-series methods. By rapidly pinpointing and classifying faults with high fidelity, it enhances grid resilience, reduces operational risk, and enables more stable, intelligent operation, demonstrating the value of AI-driven fault detection for future power-system reliability.