Efficient detection method for data integrity attacks in smart grid

Abstract

With the developing of the Smart Grid, false data injection attacks (FDIAs) as a typical data integrity attack successfully bypass the traditional bad data detection and identification, has a serious influence on the power system safe and reliable operation. State estimation, which is an important process in smart grid, is used in system monitoring to get optimally estimate the power grid state through analysis of the monitoring data. However, FDIAs compromising data integrity will lead to wrong decision makings in power dispatch or electric power market transactions. In this paper, focusing on the power property, we introduce an index to quantitatively measure the node voltage stability and reflect the influence of FDIAs on the power system. Then, we use an improved clustering algorithm to identify the node vulnerability level, which helps operators take measures and detect the false data injection attacks timely. Besides, one effective state forecasting detection method is proposed, which is meaningful for real-time detection of false data injection attacks. Finally, the simulation result verifies the effectiveness and performance of the proposed method.