A penalty-based adaptive secure estimation for power systems under false data injection attacks

2020 ◽  
Vol 508 ◽  
pp. 380-392
Author(s):  
Minjing Yang ◽  
Hao Zhang ◽  
Chen Peng ◽  
Yulong Wang
Keyword(s):  
Power Systems ◽  
False Data Injection ◽  
Injection Attacks

scholarly journals A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

2021 ◽  
Vol 11 (17) ◽  
pp. 8074
Author(s):  
Tierui Zou ◽  
Nader Aljohani ◽  
Keerthiraj Nagaraj ◽  
Sheng Zou ◽  
Cody Ruben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Wide Area ◽  
Network Parameter ◽  
False Data Injection ◽  
Network Parameters ◽  
Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

False Data Injection Attacks Induced Sequential Outages in Power Systems

2019 ◽  
Vol 34 (2) ◽  
pp. 1513-1523 ◽  
Author(s):  
Liang Che ◽  
Xuan Liu ◽  
Zuyi Li ◽  
Yunfeng Wen
Keyword(s):  
Power Systems ◽  
False Data Injection ◽  
Injection Attacks

A Secure Hybrid Dynamic-State Estimation Approach for Power Systems Under False Data Injection Attacks

2020 ◽  
Vol 16 (12) ◽  
pp. 7275-7286 ◽  
Author(s):  
Zahra Kazemi ◽  
Ali Akbar Safavi ◽  
Farshid Naseri ◽  
Leon Urbas ◽  
Peyman Setoodeh
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Dynamic State ◽  
False Data Injection ◽  
Injection Attacks ◽  
Dynamic State Estimation

scholarly journals Modeling and Detecting False Data Injection Attacks against Railway Traction Power Systems

2018 ◽  
Vol 2 (4) ◽  
pp. 1-29 ◽  
Author(s):  
Subhash Lakshminarayana ◽  
Teo Zhan Teng ◽  
Rui Tan ◽  
David K. Y. Yau
Keyword(s):  
Power Systems ◽  
False Data Injection ◽  
Injection Attacks ◽  
Railway Traction ◽  
Traction Power
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