PARAMETER FDI CORRECTION PHYSICS-BASED MODEL: A MACHINE LEARNING SYNTHETIC MEASUREMENT BASED APPROACH

Abstract:

Concerning smart grids real-time monitoring 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. State-of-the-art physics-model based 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 FDI correction model is presented. The overdetermined model uses a parameter 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, built on the classical weighted least squares solution, highlights potential aspects for real-life implementation.

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Citation:

T. Zou, N. Aljohani, K. Nagaraj, S. Zou, C. Ruben, A. Bretas, A. Zare and Janise McNair, "FDI Correction Physics-based Model: A Machine Learning Synthetic Measurement based Approach," Under Review.
@Article{Zou2020FDICorrection,
Title = {FDI Correction Physics-based Model: A Machine Learning Synthetic Measurement based Approach}, 
Author = {Zou, Tierui and Aljohani, Nader and Nagaraj, Keerthiraj and Zou, Sheng and Ruben, Cody and Bretas, Arturo and
Zare, Alina and McNair, Janise},  
Journal = {}, 
Volume = {},  
Year = {Under Review},  
}