False data injection attacks detection on power systems with convolutional neural network

Abstract

False data injection attack (FDIA) could manipulate measurement information collected by supervisory control and data acquisition (SCADA) system, which tempers with decisions of power grid and threatens the state estimation of smart grid. Aiming at the state estimation of smart grid, the principle of FDIA under the AC power flow model is studied and a FDIA detection model based on improved convolutional neural network CNN is constructed By adding the gate recurrent unit (GRU) to the fully connected layer in CNN, the CNN-GRU network is designed to train and update network parameters based on historical measurement data of power grid, and extract spatial and temporal characteristics of the data to implement efficient and real-time FDIA detector. Finally, in the IEEE 118 bus test systems, experiments are carried out to verify the effectiveness of the proposed method.