  An important tool grid operators use to safeguard against failures, whether
naturally occurring or malicious, involves detecting anomalies in the power
system SCADA data. In this paper, we aim to solve a real-time anomaly detection
problem. 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 by using a
combination of current and historical data for anomaly detection 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. This
algorithm converts the real-time power flow measurements to context-agnostic
values, which allows us to analyze measurement coming from different grid
contexts in an aggregate fashion, enabling us to derive a unified statistical
model that becomes the basis of anomaly detection. 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.
