scholarly journals Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks

2012 ◽  
Vol 3 (3) ◽  
pp. 1362-1370 ◽  
Author(s):  
Gabriela Hug ◽  
Joseph Andrew Giampapa
Keyword(s):  
State Estimation ◽  
Vulnerability Assessment ◽  
Cyber Attacks ◽  
False Data Injection

scholarly journals The usage of power system multi-model forecasting aided state estimation for cyber attack detection

2022 ◽  
Vol 23 (5) ◽  
pp. 13-23
Author(s):  
I. A. Lukicheva ◽  
A. L. Kulikov
Keyword(s):  
Power System ◽  
State Estimation ◽  
Estimation Algorithm ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Protective Measure ◽  
Cyber Attack ◽  
False Data Injection ◽  
Forecasting Models ◽  
Autoregression Model

THE PURPOSE. Smart electrical grids involve extensive use of information infrastructure. Such an aggregate cyber-physical system can be subject to cyber attacks. One of the ways to counter cyberattacks is state estimation. State Estimation is used to identify the present power system operating state and eliminating metering errors and corrupted data. In particular, when a real measurement is replaced by a false one by a malefactor or a failure in the functioning of communication channels occurs, it is possible to detect false data and restore them. However, there is a class of cyberattacks, so-called False Data Injection Attack, aimed at distorting the results of the state estimation. The aim of the research was to develop a state estimation algorithm, which is able to work in the presence of cyber-attack with high accuracy.METHODS. The authors propose a Multi-Model Forecasting-Aided State Estimation method based on multi-model discrete tracking parameter estimation by the Kalman filter. The multimodal state estimator consisted of three single state estimators, which produced single estimates using different forecasting models. In this paper only linear forecasting models were considered, such as autoregression model, vector autoregression model and Holt’s exponen tial smoothing. When we obtained the multi-model estimate as the weighted sum of the single-model estimates. Cyberattack detection was implemented through innovative and residual analysis. The analysis of the proposed algorithm performance was carried out by simulation modeling using the example of a IEEE 30-bus system in Matlab.RESULTS. The paper describes an false data injection cyber attack and its specific impact on power system state estimation. A Multi - Model Forecasting-Aided State Estimation algorithm has been developed, which allows detecting cyber attacks and recovering corrupted data. Simulation of the algorithm has been carried out and its efficiency has been proved.CONCLUSION. The results showed the cyber attack detection rate of 100%. The Multi-Model Forecasting-Aided State Estimation is an protective measure against the impact of cyber attacks on power system.

scholarly journals A Bi-Level Model for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

2021 ◽  
Vol 11 (14) ◽  
pp. 6540
Author(s):  
Nader Aljohani ◽  
Arturo Bretas
Keyword(s):  
State Estimation ◽  
State Of The Art ◽  
Real Life ◽  
Measurement Model ◽  
Cyber Attacks ◽  
Model Parameters ◽  
System State ◽  
Power System State Estimation ◽  
False Data Injection ◽  
Level Model

Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

scholarly journals Review of Cyber-Physical Attacks in Smart Grids: A System-Theoretic Perspective

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1153
Author(s):  
Francesco Liberati ◽  
Emanuele Garone ◽  
Alessandro Di Giorgio
Keyword(s):  
Smart Grid ◽  
State Estimation ◽  
Smart Grids ◽  
False Data Injection ◽  
Injection Attacks ◽  
The Common ◽  
Dynamical Properties ◽  
Attack Models

This paper presents a review of technical works in the field of cyber-physical attacks on the smart grid. The paper starts by discussing two reference mathematical frameworks proposed in the literature to model a smart grid under attack. Then, a review of cyber-physical attacks on the smart grid is presented, starting from works on false data injection attacks against state estimation. The aim is to present a systematic and quantitative discussion of the basic working principles of the attacks, also in terms of the inner smart grid vulnerabilities and dynamical properties exploited by the attack. The main contribution of the paper is the attempt to provide a unifying view, highlighting the fundamental aspects and the common working principles shared by the attack models, even when targeting different subsystems of the smart grid.

scholarly journals Reinforcement Learning Based Detection for State Estimation Under False Data Injection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Weiliang Jiang ◽  
Wen Yang ◽  
Jiayu Zhou ◽  
Wenjie Ding ◽  
Yue Luo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Estimation ◽  
False Data Injection
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