Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

TL;DR

This paper introduces a model-agnostic, real-time algorithm using Koopman mode decomposition to detect false data attacks in power grids, effectively distinguishing natural oscillations from malicious injections during transient conditions.

Contribution

It presents a novel Koopman mode decomposition-based method for real-time detection of false data attacks in power systems, capturing nonlinear oscillations and revealing spatial embeddings of anomalies.

Findings

Effective detection of false data attacks during transients

Successful application on IEEE 68-bus system with synthetic attacks

Koopman modes reveal spatial and dynamic characteristics of oscillations

Abstract

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.