Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning

TL;DR

This paper introduces a deep learning-based method using LSTM-DAE to identify and correct false data injection attacks in AC state estimation, enhancing detection and prevention capabilities in power systems.

Contribution

The paper presents a novel LSTM-DAE approach for both identifying and correcting FDIAs in AC state estimation, addressing a gap in existing detection-focused methods.

Findings

Successfully identified attacked states with high accuracy

Effectively corrected corrupted measurements in IEEE 30 system

Demonstrated robustness of the method against FDIAs

Abstract

recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to detect attacks. Inspired by these advancements, we have developed a new methodology for not only identifying AC FDIAs but, more importantly, for correction as well. Our methodology utilizes a Long-Short Term Memory Denoising Autoencoder (LSTM-DAE) to correct attacked-estimated states based on the attacked measurements. The method was evaluated using the IEEE 30 system, and the experiments demonstrated that the proposed method was successfully able to identify the corrupted states and correct them with high accuracy.