A detection based on particle filtering and multivariate time-series anomaly detection via graph attention network for automatic voltage control attack in smart grid

Abstract

The Automatic Voltage Control (AVC) attack is a novel attack that targets voltage control instructions sent to generators from the dispatching center. A successful AVC attack can manipulate reactive power or terminal voltage of generators without being detected, causing the voltages of pilot buses to deviate from the reference values received from the dispatching center. This poses a threat to the safe and stable operation of power systems. This paper proposed a detection based on Particle Filtering (PF) and multivariate time-series anomaly detection via graph attention network (MTAD-GAT). Although each method can detect AVC attacks independently, the coordination of the two methods can be more effective. PF and MTAD are utilized to predict the voltage changes of the pilot bus in the next moment. To combine them, adaptive weights are employed, and an adaptive hybrid prediction can be calculated. The moment can be identified as attacked if the absolute value of the difference between the pilot bus voltage and the reference value exceeds a threshold automatically chosen by Peaks Over Thresholds (POT) theory. The proposed method has been validated through simulations on the IEEE 39-bus 6-partition Coordinated Secondary Voltage Control (CSVC) system and has shown to be effective.