Unbalanced power anomaly detection model based on improved transformer and countermeasure encoder

Current intelligent grid anomaly detection faces challenges such as low minority-class recognition due to imbalanced data, high computational complexity in long-sequence processing, and model bias from scarce anomaly samples. To address these, we propose a hybrid architecture combining an enhanced Transformer with an Adversarial Autoencoder (AAE). We introduce a Locality-Sensitive Hashing (LSH) attention mechanism using Focal Loss with Temperature (FLT) to cluster similar features. A dynamic weighting module, implemented via a Spatial-Temporal Feature Disentanglement Network (STFDN), adaptively adjusts gradients by category. Our approach reduces memory usage for node sequences from 18.7GB to 8.9GB (52.4% less) via Spectral Normalization. Under Wasserstein distance constraints, the model achieves an FID score of 28.4, a 10.4% improvement. An innovative dynamic temperature scaling strategy elevates the AUPRC to 0.837 on the SGSC dataset. Tests on the UK-DALE dataset show an F1-score of 89.3% with 183ms inference latency, meeting edge deployment requirements. This research offers a promising new generation of automated detection tools for grid operation and maintenance.