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

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

Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USA
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Author to whom correspondence should be addressed.
Academic Editor: Yosoon Choi
Appl. Sci. 2021, 11(17), 8074; https://doi.org/10.3390/app11178074
Received: 29 July 2021 / Revised: 26 August 2021 / Accepted: 29 August 2021 / Published: 31 August 2021
(This article belongs to the Special Issue Intelligent Fault Diagnosis of Power System)
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. View Full-Text
Keywords: monitoring systems; false data injection; database of the network parameters; cyber–physical security; parameter cyber-attack correction monitoring systems; false data injection; database of the network parameters; cyber–physical security; parameter cyber-attack correction
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MDPI and ACS Style

Zou, T.; Aljohani, N.; Nagaraj, K.; Zou, S.; Ruben, C.; Bretas, A.; Zare, A.; McNair, J. A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach. Appl. Sci. 2021, 11, 8074. https://doi.org/10.3390/app11178074

AMA Style

Zou T, Aljohani N, Nagaraj K, Zou S, Ruben C, Bretas A, Zare A, McNair J. A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach. Applied Sciences. 2021; 11(17):8074. https://doi.org/10.3390/app11178074

Chicago/Turabian Style

Zou, Tierui, Nader Aljohani, Keerthiraj Nagaraj, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare, and Janise McNair. 2021. "A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach" Applied Sciences 11, no. 17: 8074. https://doi.org/10.3390/app11178074

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