Abstract
A smart grid is a complex system using power transmission and distribution networks to connect electric power generators to consumers across a large geographical area. Due to their heavy dependencies on information and communication technologies, smart grid applications, such as state estimation, are vulnerable to various cyber-attacks. False data injection attacks (FDIA), considered as the most severe threats for state estimation, can bypass conventional bad data detection mechanisms and render a significant threat to smart grids. In this paper, we propose a novel FDIA detection mechanism based on a wide and recurrent neural networks (RNN) model to address the above concerns. Simulations over IEEE 39-bus system indicate that the proposed mechanism can achieve a satisfactory FDIA detection accuracy.
Keywords
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Acknowledgment
This work was partially supported by the US National Science Foundation under grant IIS-1741279, and the National Science Foundation of China under grants 61832012, 61771289, and 61672321.
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Wang, Y., Chen, D., Zhang, C., Chen, X., Huang, B., Cheng, X. (2019). Wide and Recurrent Neural Networks for Detection of False Data Injection in Smart Grids. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_27
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DOI: https://doi.org/10.1007/978-3-030-23597-0_27
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