Anomaly Detection in Power Grids via Context-Agnostic Learning
SangWoo Park Amritanshu Pandey
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.