Reduction of Frequency Disruption During Cyber-Attack in the Power System

2020 ◽  
Author(s):  
Muhammad Musleh Uddin ◽  
Md. Monirul Kabir
Keyword(s):  
Power System ◽  
Cyber Attack

Load frequency control in the presence of simultaneous cyber-attack and participation of demand response program

2022 ◽  
pp. 014233122110686
Author(s):  
Seyed Hossein Rouhani ◽  
Hamed Mojallali ◽  
Alfred Baghramian
Keyword(s):  
Power System ◽  
Demand Response ◽  
Sliding Mode ◽  
Load Frequency Control ◽  
Frequency Regulation ◽  
Cyber Attack ◽  
Smart Power ◽  
False Data Injection ◽  
Load Disturbance ◽  
Communication Time

Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate the whole or a part of the load disturbance based on the available electrical power in the aggregators considering communication time delay. Finally, active disturbance rejection control is modified and introduced to remove the false data injection cyber-attack and control the uncompensated load disturbance. The salp swarm algorithm is used to design the parameters. The results of several simulation scenarios indicate the efficient performance of the proposed method.

scholarly journals A deep learning‐based classification scheme for cyber‐attack detection in power system

2021 ◽  
Author(s):  
Yucheng Ding ◽  
Kang Ma ◽  
Tianjiao Pu ◽  
Yingxing Wang ◽  
Ran Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Classification Scheme ◽  
Attack Detection ◽  
Cyber Attack

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.

Machine learning for power system disturbance and cyber-attack discrimination

2014 ◽  
Author(s):  
Raymond C. Borges Hink ◽  
Justin M. Beaver ◽  
Mark A. Buckner ◽  
Tommy Morris ◽  
Uttam Adhikari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Cyber Attack
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