A Tampering Detection Method for Power Monitoring Data Based on Kalman Filter

Electric energy is an important resource related to the national economy and people's livelihood and ensuring the reliable supply of electricity plays a significant role in the healthy and stable development of the national economy. The integration of network information technology into the power industry brings intelligence and convenience, which also increases the potential security management risks in the power cyber-physical system (CPS). False data injection attack (FDIA), as one of the forms of network attacks, it can evade the detection of bad data in the state estimation of power CPS, thereby tampering with state values and seriously affecting the energy management system of power system dispatch centers. This article studies how to effectively detect false data injected into the power CPS. The main content is as follows: Firstly, this article uses the principle of FDIA to establish an attack model and obtain an attack vector to launch an attack on the power CPS. It researches a false data injection attack detection method based on dynamic estimation results combined with consistency testing and determines its detection process. Then, this article adopts a detection method based on unscented Kalman filtering (UKF) to detect FDIA, and it compares the detection effect with that based on cubature Kalman filter (CKF). The results show that the method can effectively detect false data, and its performance is superior to the CKF detection method.