Detection of false data injection attacks in smart grid based on adaptive inhibition unscented Kalman filter

Author:

Zhang Guoqing,Gao Wengen,Zhu Jiaming,Li Yaru

Abstract

Abstract False data injection attacks (FDIAs) cause incorrect system states by tampering with measurements, seriously affecting the EMS’s control process. However, the well-designed FDIAs can bypass traditional bad data detection (BDD) mechanisms. Aiming at the challenge, we improve the unscented Kalman filter and combine AIUKF with weighted least squares (WLS) to detect FDIAs. Utilizing the different convergence rates of the two estimators, the cosine similarity is introduced for FDIA detection. Various test conditions in IEEE-14-bus are simulated to underline the capability of AIUKF in state estimation. The results indicate that the proposed detection approach is superior for detecting FDIAs.

Publisher

IOP Publishing

Reference10 articles.

1. Detection and Localization of False Data Injection Attacks in Smart Grid Based on Joint Maximum a Posteriori-Maximum Likelihood;Zhang;IEEE Access,2023

2. Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter;Manandhar;IEEE Trans. Control. Netw. Syst.,2014

3. Detection of False Data Injection Attacks in Smart Grid Communication Systems;Rawat;IEEE Signal Process Lett.,2015

4. Detection of fake data injection attack in smart grid based on adaptive Kalman filter;Luo;Automation,2022

5. Detection of false data injection attacks in power grid based on XGBoost and Unscented Kalman Filter adaptive Hybrid Prediction;Liu;Proceedings of the CSEE,2021

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