Multi-data classification detection in smart grid under false data injection attack based on Inception network

Abstract

During operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. Therefore, this article aims to propose a multi-data classification detection model to differentiate the data of regular operation, faults, and FDIAs when the smart grid suffers FDIAs. Due to the unbalanced number of different kinds of samples in the dataset, Affinitive Borderlinen SMOTE is used to pre-process the data by oversampling to improve the training accuracy. A multi-data detection model based on the Inception network is established, and the overall structure of the network and the individual Inception modules are given. A small power system is an example of simulating a smart grid suffering from FDIAs. The designed classification detection model is simulated, validated, and compared with two-dimensional convolutional neural networks and existing research results. The qualitative analysis of the evaluation metrics can show that the Inception network model has high accuracy and real-time performance for detecting different data.