Power System Anomaly Detection via Context-Aware Learning

An important tool grid operators use to safeguard against failures involves detecting anomalies in the power system SCADA data. This paper solves the underlying real-time anomaly detection problem for power grid operation. Given time-series measurement values coming from a fixed set of sensors on the grid, can we identify anomalies in the network topology or measurement data? Existing methods, primarily optimization-based, mostly use only a single snapshot of the measurement values and do not scale well with the network size. Recent data-driven ML techniques have shown promise but generally do not consider physical attributes like the impact of topology or load/generation changes on sensor measurements and thus cannot accommodate regular context-variability in the historical data. To address this gap, we propose a novel context-aware anomaly detection algorithm, GridCAL, that considers the effect of regular topology and load/generation changes. Through numerical simulations on networks up to 2383 nodes, we show that our approach is accurate, outperforming state-of-the-art approaches, and is computationally efficient.