Identification of Intraday False Data Injection Attack on DER Dispatch Signals

TL;DR

This paper examines the vulnerability of power grids with high renewable energy to intraday false data injection attacks on DER dispatch signals and proposes a kernel support vector regression detection model tested on a 187-bus system.

Contribution

It introduces a novel detection model using kernel support vector regression for identifying intraday FDI attacks on DER dispatch signals.

Findings

The FDI attack can significantly disrupt grid operations.

The SVR-based detection model effectively identifies false data injections.

Numerical experiments validate the model's performance on a test system.

Abstract

The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operational challenges. This paper investigates the vulnerability of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.