Study of a detection system for false data injection attacks based on AMPSO-BP neural networks

False data injection attacks (FDIAs) are new attack manners for power system state estimation in smart grid. FDIAs often bypass the monitoring and defense of the power system, and the security of the whole system will be compromised after the wrong system running state is transferred to the dispatching center. To address this issue, we put forward a new FDIAs detection system. Firstly, the system preliminarily filters the power system measurements through using the trust white list algorithm. Next, to further improve the abnormal database, self-adaptive particle swarm optimization-back propagation (AMPSO-BP) neural networks are utilized to screen the measurements again. Finally, FDIAs are detected relying on the improved database. Simulation tests demonstrate the accuracy of the proposed detection system.