A resilient method for smart power grid synchronization estimation against partial missing measurements

With the increasing demand for power system stability and resilience, effective real-time tracking plays a crucial role in smart grid synchronization. However, most studies have focused on measurement noise, while they seldom think about the problem of measurement data loss in smart power grid synchronization. To solve this problem, a resilient fault-tolerant extended Kalman filter (RFTEKF) is proposed to dynamically track voltage amplitude, voltage phase angle and frequency. First, the positive sequence fast estimation model of three-phase unbalanced network is established. Secondly, the loss phenomenon of measurements occurs in a random way and the randomness of data loss is defined by the discrete distribution of the interval [0,1]. Subsequently, a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed by using time-stamp technique to acquire partial data loss information. Finally, extensive simulation results manifest that the proposed RFTEKF can synchronize the smart grid significantly more effectively than the traditional extended Kalman filter (EKF).