Anomaly Detection in Power Grids via Context-Agnostic Learning

• URL: http://arxiv.org/abs/2404.07898v1
• Date: Thu, 11 Apr 2024 16:37:01 GMT
• Title: Anomaly Detection in Power Grids via Context-Agnostic Learning
• Authors: SangWoo Park, Amritanshu Pandey,
• Abstract summary: 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? Recent data-driven ML techniques have shown promise by using a combination of current and historical data for anomaly detection. We propose a novel context-aware anomaly detection algorithm, GridCAL, that considers the effect of regular topology and load/generation changes.
• Score: 4.865842426618145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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