Detection of false data injection attacks using unscented Kalman filter

It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software.