False Data Injection Attack Detection Method Based on Deep Learning With Multi-Scale Feature Fusion

Abstract

Cyber-attacks, especially the false data injection attack (FDIA), are gradually becoming a common way to threaten the regular operation of power grid. However, the FDIA is challenging to detect because it prevents the bad data detection mechanism in the energy management system from destroying the integrity of measurement information. Aiming at the problem of the FDIA detection in smart grids, this paper presents a FDIA detection method based on deep learning with multi-scale feature fusion. First, the improved convolution neural network (ICNN) is used to predict measurement data by combining convolution neural network with the Inception v1 module. Then, the attention mechanism is introduced into the ICNN to extract and fuse full and partial features of measurement data. By fitting the function between measurement and state vectors, the state data are generated with predicted measurement data. Eventually, the threshold of divergence is obtained to determine whether the FDIA occurs or not by the difference in probability distribution between predicted and actual state vectors. The performance of the proposed method is evaluated in the IEEE 14-node and 39-node test systems. The results show that the proposed method can accurately detect the existence of FDIA in time. This method has definite robustness to noise and distributed generation switching.

Keywords

• False data injection attack
• convolution neural network
• feature fusion
• multi-scale convolution
• dynamic state estimation