Transformers can outperform traditional deep learning for time-series fault detection in power systems, especially as grids become more complex wit...
FaultXformer uses a Transformer model to detect and locate electrical faults in power grids using real-time sensor data. It processes current measurements in two stages—first extracting temporal patterns, then classifying fault types and pinpointing locations—achieving 98%+ accuracy and outperforming traditional deep learning approaches like CNNs and LSTMs.