Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter

Abstract

An accurate power state is the basis of the normal functioning of the smart grid. However, false data injection attacks (FDIAs) take advantage of the vulnerability in the bad data detection mechanism of the power system to manipulate the process of state estimation. By attacking the measurements, then affecting the estimated state, FDIAs have become a serious hidden danger that affects the security and stable operation of the power system. To address the bad data detection vulnerability, in this paper, a false data attack detection method based on weighted least squares (WLS) and an adaptive interpolation extended Kalman filter (AIEKF) is proposed. On the basis of applying WLS and AIEKF, the Euclidean distance is used to calculate the deviation values of the two-state estimations to determine whether the current moment is subjected to a false data injection attack in the power system. Extensive experiments were conducted to simulate an IEEE-14-bus power system, showing that the adaptive interpolation extended Kalman filter can compensate for the deficiency in the bad data detection mechanism and successfully detect FDIAs.

Multilevel Distributed Linear State Estimation Integrated with Transmission Network Topology Processing

“…Based on the availability of the breaker current measurement, another linear relationship can be established for the breaker currents by relating the breaker current estimation to the measurements with an identity matrix, as shown in (5). z breaker refers to the breaker current measurements, I is the identity matrix, and r breaker is the breaker current measurement residue.…”

“…Traditional state estimation was introduced to the power system by Fred Schweppe in 1970 in [1][2][3] to process available imperfect information (noise, bad data, or false data) [4,5] and produces the best possible estimates for the state variables in consideration. However, the traditional static state estimation is inefficient in estimating state variables in modern power systems.…”