Residual-Based Detection of Attacks in Cyber-Physical Inverter-Based Microgrids

TL;DR

This paper introduces a nonlinear residual-based observer method to detect stealthy integrity attacks in cyber-physical microgrids, ensuring stability and operational safety.

Contribution

It presents a novel residual-based detection approach that incorporates stability constraints for cyber-physical microgrids, addressing a key research gap.

Findings

Effective detection of stealthy attacks demonstrated in case studies

Maintains stability and operational constraints during attack detection

Applicable to microgrids with multiple distributed generators

Abstract

This paper discusses the challenges faced by cyber-physical microgrids (MGs) due to the inclusion of information and communication technologies in their already complex, multi-layered systems. The work identifies a research gap in modeling and analyzing stealthy intermittent integrity attacks in MGs, which are designed to maximize damage and cancel secondary control objectives. To address this, the paper proposes a nonlinear residual-based observer approach to detect and mitigate such attacks. In order to ensure a stable operation of the MG, the formulation then incorporates stability constraints along with the detection observer. The proposed design is validated through case studies on a MG benchmark with four distributed generators, demonstrating its effectiveness in detecting attacks while satisfying network and stability constraints.