Detection of False Data Injection Attack in smart grid based on iterative Kalman filter

Aiming at the difficulties on detection of false data injection attack (FDIA) in smart grid, a novel approach called state residual analysis which is based on iterated extended Kalman filter (IEKF) is explained in the article. Firstly, the classic power system is modeled and the principle of FDIA is analyzed. Then, the traditional extended Kalman filter (EKF) is improved combined with the framework of Kalman filter as follows. By judging the approximation degree to the state of each iteration, the weighted statistical average method is designed to determine the new iteration value to update the state and measurement. After that, the state of the system is analyzed based on the above dynamic estimation algorithm IEKF. It is deduced that there is a change in the estimated state of the system which can be used as the criterion of attack detection due to the existence of covert FDIA. If the change exceeds the detection threshold, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to confirm the approaches mentioned.