Vector correlation learning and pairwise optimization feature selection for false data injection attack detection in smart grid

Abstract

Abstract This paper mainly studies vector-related learning and pairwise optimization feature selection for false data injection attack detection in smart grid. In order to provide experimental detection of false data injection attacks, this article will conduct a detailed analysis of the power system segmentation. This paper comprehensively considers the similarity of nodes, security control and confidentiality strategies to complete the optimal partition. Next, we preprocess the measurement data. In order to improve the adaptability of the algorithm structure to the grid structure and further improve the accuracy and convergence speed of the algorithm output, a differential evolution algorithm with swarm intelligence is proposed. Obtain a higher-precision state estimate useful for detecting bad data. In the experiment of this article, if false data account for 20% of all data, the detection accuracy exceeds 75%. As the number of experimental groups increases, the detection accuracy of only one type of false data that does not meet the rules will continue to increase, but the detection accuracy of other types of false data will not change much, but the overall detection accuracy will become higher. Experimental results show that the detection framework can not only effectively detect and identify false data injection attacks on multiple bus nodes, but also has high detection accuracy and can effectively recover false data.