Cognitive Dynamic System for AC State Estimation and Cyber-Attack Detection in Smart Grid

Mapping Intimacies ◽

10.5772/intechopen.94093 ◽

2020 ◽

Author(s):

Mohammad Irshaad Oozeer ◽

Simon Haykin

Keyword(s):

Smart Grid ◽

Dynamic System ◽

State Estimation ◽

Attack Detection ◽

Cyber Attacks ◽

Data Detection ◽

Cyber Attack ◽

Bad Data Detection ◽

Cycle Basis ◽

Iterative Procedures

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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Stacked Autoencoder Framework of False Data Injection Attack Detection in Smart Grid

Mathematical Problems in Engineering ◽

10.1155/2021/2014345 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Liang Chen ◽

Songlin Gu ◽

Ying Wang ◽

Yang Yang ◽

Yang Li

Keyword(s):

Smart Grid ◽

Smart Grids ◽

Control Strategies ◽

Attack Detection ◽

Cyber Attacks ◽

Data Detection ◽

Layer By Layer ◽

Monitoring And Control ◽

Bad Data Detection ◽

False Data Injection

The advanced communication technology provides new monitoring and control strategies for smart grids. However, the application of information technology also increases the risk of malicious attacks. False data injection (FDI) is one kind of cyber attacks, which cannot be detected by bad data detection in state estimation. In this paper, a data-driven FDI attack detection framework of the smart grid with phasor measurement units (PMUs) is proposed. To enhance the detecting accuracy and efficiency, the multiple layer autoencoder algorithm is applied to abstract the hidden features of PMU measurements layer by layer in an unsupervised manner. Then, the features of the measurements and corresponding labels are taken as inputs to learn a softmax layer. Last, the autoencoder and softmax layer are stacked to form a FDI detection framework. The proposed method is applied on the IEEE 39-bus system, and the simulation results show that the FDI attacks can be detected with higher accuracy and computational efficiency compared with other artificial intelligence algorithms.

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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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Emerging Challenges in Smart Grid Cybersecurity Enhancement: A Review

Energies ◽

10.3390/en14051380 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1380

Author(s):

Fazel Mohammadi

Keyword(s):

Smart Grid ◽

Power Systems ◽

Cost Effective ◽

Attack Detection ◽

Cyber Attacks ◽

Point Of View ◽

Computational Time ◽

Cyber Attack ◽

Factors Affecting ◽

Detection And Identification

In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is no all-inclusive solution to fit all power systems requirements. Therefore, the recently proposed cyber-attack detection and identification methods are quantitatively compared and discussed.

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