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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Wide and Recurrent Neural Networks for Detection of False Data Injection in Smart Grids

verfasst von : Yawei Wang, Donghui Chen, Cheng Zhang, Xi Chen, Baogui Huang, Xiuzhen Cheng

Erschienen in: Wireless Algorithms, Systems, and Applications

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Wide and Recurrent Neural Networks for Detection of False Data Injection in Smart Grids
verfasst von
Yawei Wang
Donghui Chen
Cheng Zhang
Xi Chen
Baogui Huang
Xiuzhen Cheng
Copyright-Jahr
2019
Buch
Wireless Algorithms, Systems, and Applications

Print ISBN: 978-3-030-23596-3

Electronic ISBN: 978-3-030-23597-0

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-23597-0_27

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