A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach
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...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/17/8074 |
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| author | Tierui Zou Nader Aljohani Keerthiraj Nagaraj Sheng Zou Cody Ruben Arturo Bretas Alina Zare Janise McNair |
| author_facet | Tierui Zou Nader Aljohani Keerthiraj Nagaraj Sheng Zou Cody Ruben Arturo Bretas Alina Zare Janise McNair |
| author_sort | Tierui Zou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:15:20Z |
| format | Article |
| id | doaj.art-19915b666dcf4cddbfb43678dc3a321c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T08:15:20Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-19915b666dcf4cddbfb43678dc3a321c2023-11-22T10:21:06ZengMDPI AGApplied Sciences2076-34172021-08-011117807410.3390/app11178074A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based ApproachTierui Zou0Nader Aljohani1Keerthiraj Nagaraj2Sheng Zou3Cody Ruben4Arturo Bretas5Alina Zare6Janise McNair7Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USADepartment of Electrical & Computer Engineering, University of Florida, Gainesville, FL 32611-6200, USAConcerning 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.https://www.mdpi.com/2076-3417/11/17/8074monitoring systemsfalse data injectiondatabase of the network parameterscyber–physical securityparameter cyber-attack correction |
| spellingShingle | Tierui Zou Nader Aljohani Keerthiraj Nagaraj Sheng Zou Cody Ruben Arturo Bretas Alina Zare Janise McNair A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach Applied Sciences monitoring systems false data injection database of the network parameters cyber–physical security parameter cyber-attack correction |
| title | A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach |
| title_full | A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach |
| title_fullStr | A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach |
| title_full_unstemmed | A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach |
| title_short | A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach |
| title_sort | network parameter database false data injection correction physics based model a machine learning synthetic measurement based approach |
| topic | monitoring systems false data injection database of the network parameters cyber–physical security parameter cyber-attack correction |
| url | https://www.mdpi.com/2076-3417/11/17/8074 |
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