Ensemble CorrDet with adaptive statistics for bad data detection

Nagaraj, Keerthiraj; Zou, Sheng; Ruben, Cody; Dhulipala, Surya Chandan; Starke, Allen; Bretãs, Arturo S.; Zare, Alina; McNair, Janise

doi:10.1049/iet-stg.2020.0029

Cited by 22 publications

(35 citation statements)

References 44 publications

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“…We assume that the current day measurements do not deviate from the historical trend by a huge margin and the training data does not have any anomalies. The historical data could be analyzed through bad detection techniques such as [18] for the presence of anomalies and then only the data that does not contain bad data could be used for ML model training.…”

Section: Linear Regression Prediction Modelmentioning

confidence: 99%

“…Meanwhile, gross error analysis is performed in stage-II WLS state estimation. In this stage, FDI detection and identification is taken place [18,19]. Upon detecting and identifying parameter attacks in the network, a parameter FDI correction model is solved.…”

Section: Framework For Parameter Fdi Correctionmentioning

confidence: 99%

“…Then, two synthetic measurements P 32-23 and Q 23-32 are obtained and augmented from SM dataset. Parameter correction is processed by using 2 existing measurements and two synthetic measurements in the presented parameter FDI correction model (18). Results of such correction are presented in Figure 3.…”

Section: Parameter Attack Scenario Imentioning

confidence: 99%

“…Further, research that involves machine learning so far focuses on either creating more accurate SE results or aiding in the detection of measurement FDI attacks [9][10][11][12][13][14][15][16]. In the authors' previous works [17,18], hybrid data-driven and physics-based methods for anomaly detection on the SG are developed, but they still only deal with measurement FDI attacks. On the other hand, there is no real-time monitoring for these network parameters which are used in SE process.…”

Section: Introductionmentioning

confidence: 99%

“…This scenario characterizes a parameter cyber-attack on line 47-69 [19]. After identification, the net system parameter are corrected using the presented model in (18). Still, only flow P 69-47 and Q 69-47 are provided in current measurements configuration.…”

mentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Linear Regression Prediction Modelmentioning

confidence: 99%

Section: Framework For Parameter Fdi Correctionmentioning

confidence: 99%

Section: Parameter Attack Scenario Imentioning

confidence: 99%