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AC False Data Injection Attack Based on Robust Tensor Principle Component Analysis
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AI
DOI：10.1109/TII.2024.3390389.png)
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Abstract
En 中文
False data injection attacks (FDIAs) represent a significant threat to power grid cybersecurity, designed to manipulate crucial measurement data and thereby compromise the operation of power grids. This article proposes an ac FDIA method based on tensor principle component analysis (TPCA), requiring no prior knowledge of system parameters. The goal of the proposed approach is to produce false data that can break through the bad data detection (BDD) of realistic ac state estimation. Specifically, ac state estimation model is transformed into a tensor representation, encapsulating measurement variables, state variables, and system parameters as a combination of multiple tensor products. Following this, by formulating multiple measurement data into a tensor, TPCA is used to decompose the measurement data tensor to obtain a space of matrices. Subsequently, the vector of false data ensuring the stealthiness is produced by finding a rank-1 approximation of matrices in this space. Notably, the proposed method distinguishes itself from existing parameter-free FDIA methods by eschewing any simplification or approximation of ac state estimation model. Numerous cases in IEEE 5, 14, 57, 118, 300-bus, European 1354-bus, and Polish 3120-bus testing systems provide substantial evidence that the proposed approach can obtain higher attack successful rate. It achieves 99.3% attack successful rate on average against the common chi(BDD)-B-2 with 0.9 confidence level. And compared with existing methods, the attack successful rate improves 4%, 2%, 13%, 11%, 10%, and 27% in these six systems, respectively.
Keywords:
AC model
false data injection attack (FDIA)
parameters-free
tensor principle component analysis (TPCA)
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