Data-driven Physics-based Solution for False Data Injection Diagnosis in Smart Grids

Trevizan, Rodrigo D.; Ruben, Cody; Nagaraj, Keerthiraj; Ibukun, Layiwola L.; Starke, Allen; Bretãs, Arturo S.; McNair, Janise; Zare, Alina

doi:10.1109/pesgm40551.2019.8974027

Cited by 24 publications

(26 citation statements)

References 11 publications

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“…For each testing sample, we add the normalised decision scores from the state estimator and ECD detector, and create a new decision score termed as fusion decision score (Ψ fusion, z k ). Fusion decision scores, which are calculated as shown in (16), are compared with ground truth values to show the improvement in fused model performance compared to individual detectors…”

Section: Fusion Of Physics Model-based Data-driven Detection Methodsmentioning

confidence: 99%

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Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

et al. 2020

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This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real time monitoring becomes more vulnerable to cyber attacks like false data injections (FDI). Although smart grids cyber-physical security has an extensive scope, this paper focuses on FDI attacks, which are modeled as bad data. State of the art strategies for FDI detection in real time monitoring rely on physics model-based weighted least squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modeling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this paper presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118 bus system.

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Section: Fusion Of Physics Model-based Data-driven Detection Methodsmentioning

confidence: 99%

“…Thus they are complementary solutions. Considering such a rationale, in [16], a previous work of the authors, an extended Chi-squared test using information from PSSE and a data-driven CorrDet algorithm [9] is presented.…”

Section: Introductionmentioning

confidence: 99%

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

et al. 2020

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“…Each time a new sample is detected as normal, mean and covariance matrices can be updated using the Woodbury Matrix Identity [41–43]. CD algorithm with fixed statistics was proposed for FDI detection in a dataset where the load profile was kept constant, but with random white noise added to the system in [25]. The authors used fixed statistics in this work as the load profile of the power system does not change for different buses over time.…”

Section: Background Informationmentioning

confidence: 99%

“…In previous work [25, 26], the anomaly detection threshold is a fixed value estimated from the initialisation stage and not updated with new incoming data. Specifically, the standard deviation and mean of squared Mahalanobis distance values of initial k normal samples are calculated first.…”

Section: Background Informationmentioning

confidence: 99%

“…These studies are mainly based on neural network, deep learning and fuzzy clustering approaches [21–24]. More recently, physics‐model based solutions have been integrated with data‐driven solutions [25, 26] to take advantage of the temporal characteristics of real‐time data. However, these solutions struggle with the fast‐changing state of power systems.…”

Section: Introductionmentioning

confidence: 99%

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