State Estimation for Attack Detection in Vehicle Platoon using VANET and Controller Model

The work presented in this chapter is an extension of our previous research of bringing together the Cognitive Dynamic System (CDS) and the Smart Grid (SG) by focusing on AC state estimation and Cyber-Attack detection. Under the AC power flow model, state estimation is complex and computationally expensive as it relies on iterative procedures. On the other hand, the False Data Injection (FDI) attacks are a new category of cyber-attacks targeting the SG that can bypass the current bad data detection techniques in the SG. Due to the complexity of the nonlinear system involved, the amount of published works on AC based FDI attacks have been fewer compared to their DC counterpart. Here, we will demonstrate how the entropic state, which is the objective function of the CDS, can be used as a metric to monitor the grid’s health and detect FDI attacks. The CDS, acting as the supervisor of the system, improves the entropic state on a cycle to cycle basis by dynamically optimizing the state estimation process through the reconfiguration of the weights of the sensors in the network. In order to showcase performance of this new structure, computer simulations are carried out on the IEEE 14-bus system for optimal state estimation and FDI attack detection.

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False Data Injection Attack Detection and Improved WLS Power System State Estimation Based on Node Trust

Journal of Electrical Engineering and Technology ◽

10.1007/s42835-021-00923-1 ◽

2021 ◽

Author(s):

Zhengwei Qu ◽

Jianxuan Zhang ◽

Yunjing Wang ◽

Popov Maxim Georgievitch ◽

Kai Guo

Keyword(s):

Power System ◽

State Estimation ◽

Attack Detection ◽

System State ◽

Power System State Estimation ◽

False Data Injection ◽

False Data Injection Attack

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Self Attack Detection and State Estimation Algorithm in Distributed Observer System under Combination Attack

2020 59th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc42340.2020.9304121 ◽

2020 ◽

Author(s):

Shotaro Sato ◽

Toru Namerikawa ◽

Zhihua Qu

Keyword(s):

State Estimation ◽

Estimation Algorithm ◽

Attack Detection ◽

Observer System

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

Proceedings of the higher educational institutions ENERGY SECTOR PROBLEMS ◽

10.30724/1998-9903-2021-23-5-13-23 ◽

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.

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