Detecting, locating, & quantifying false data injections utilizing grid topology through optimized D-FACTS device placement

Abstract

Power grids are monitored by gathering data through remote sensors and estimating the state of the grid. Bad data detection schemes detect and remove poor data. False data is a special type of data injection designed to evade typical bad data detection schemes and compromise state estimates, possibly leading to improper control of the grid. Topology perturbation is a situational awareness method that implements the use of distributed flexible AC transmission system devices to alter impedance on optimally chosen lines, updating the grid topology and exposing the presence of false data. The success of the topology perturbation for improving grid control and exposing false data in AC state estimation is demonstrated. A technique is developed for identifying the false data injection attack vector and quantifying the compromised measurements. The proposed method provides successful false data detection and identification in IEEE 14, 24, and 39-bus test systems using AC state estimation.