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

• Published in: Wireless Algorithms, Systems, and Applications pp. 335 - 345
• Main Authors: Wang, Yawei, Chen, Donghui, Zhang, Cheng, Chen, Xi, Huang, Baogui, Cheng, Xiuzhen
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Deep learning; False data injection; Smart grid
• ISBN: 9783030235963; 3030235963
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-23597-0_27

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.