Vision Transformer-Based Anomaly Detection in Smart Grid Phasor Measurement Units Using Deep Learning Models

Abstract

Modern smart grids become increasingly complex and thus require advanced anomaly detection mechanisms to guarantee system stability and reliability. New real-time measurement devices (Phasor Measurement Units (PMUs) provide high-resolution, time-synchronized voltage, frequency, and phase angle measurements. However, this is insufficient in complex and subtle anomalies; traditional threshold-based and statistical approaches fail to detect such anomalies and instead require deep learning techniques for better effectiveness. On the other hand, Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have proven to be deep learning models that can be used for PMU anomaly detection. Through hierarchical convolutional filters and self-attention mechanisms, CNNs exploit localized feature extraction counterparts to ViTs, which employ self-attention mechanisms to capture long-range dependencies of PMU data and a global contextual relationship. Although ViTs have shown capabilities for computer vision tasks, their efficacy in PMU-based anomaly detection remains under-researched, which motivates this comparative study. Based on such capability, we hypothesize that CNNs will be outperformed by ViTs in their capability to differentiate similar anomalies, especially since they can better model global spatial-temporal dependencies over CNNs’ localized feature extraction. Specifically, in this study, four CNN architectures (DenseNet201, InceptionV3, ResNet152, MobileNetV2) and four ViT models (ViT-Base-16, ViT-Base-32, ViT-Large-16, ViT-Large-32) are evaluated on a PMU anomaly dataset. Accuracy, precision, recall, F1-score, and computational efficiency are evaluated to assess these models as to whether they can perform real-time smart grid monitoring. Compared to CNN models, ViT-Large-32 achieved the highest accuracy of 98.91%, far exceeding them. ViT were more precise and recalled generation loss and motor switching anomaly numbers, while CNNs were faster to infer but more inaccurate in classification. The results confirm that ViTs achieve state-of-the-art performance in detecting PMU anomalies and are a promising grid monitoring and predictive maintenance solution. Real-time deployment of such systems remains a challenge that should be explored using computational optimizations, and hybrid CNN-ViT models for balancing sufficient performance with acceptable efficiency should be studied in large-scale innovative grid applications.

Topics & Concepts