Real-Time Power System Event Detection on Programmable Network Switches with Synchrophasor Data

Phasor Measurement Units (PMUs) deliver time-synchronized measurements that strengthen situational awareness in modern power grids. While analyzing PMU data can detect issues like voltage instability and power oscillations, conventional methods analyze PMU data only after it reaches a centralized data aggregator, introducing delays that impede real-time detection. To overcome this limitation, we integrate data plane programmability, specifically P4 (Programming Protocol-independent Packet Processors), into PMU networks, using P4's deep packet inspection and customizable parsing capabilities to detect power system events directly within the network. Our detection system features a two-layer design: a data plane for rapid packet inspection and a control plane for advanced analysis using machine learning models, such as Vector Autoregression (VAR) and Gradient Boosting Machine (GBM). Evaluation results show high detection accuracy and speed; for instance, using a dataset of 5,000 packets with 1% anomalies, our system achieved a false positive rate as low as 0.19% with a detection time of 5.02 ms, demonstrating its effectiveness for real-time event detection in PMU networks.