Hybrid data-driven physics model-based framework for enhance cyber-physical smart grid security

TL;DR

This paper introduces a hybrid framework combining data-driven and physics-based methods to improve real-time detection of false data injection attacks in smart grid cyber-physical security, validated on IEEE 118 bus system.

Contribution

It proposes a novel data fusion approach that enhances FDI attack detection robustness by integrating multiple anomaly detection methods with physics-based models.

Findings

Improved detection accuracy over traditional physics-based methods.

Effective fusion of system-level and local anomaly detection methods.

Validated robustness on IEEE 118 bus system.

Abstract

This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real time monitoring becomes more vulnerable to cyber attacks like false data injections (FDI). Although smart grids cyber-physical security has an extensive scope, this paper focuses on FDI attacks, which are modeled as bad data. State of the art strategies for FDI detection in real time monitoring rely on physics model-based weighted least squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modeling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this paper presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118 bus system.