Smart Power Grid Synchronization with Fault Tolerant Nonlinear Estimation

In order to provide more reliable state estimation addressing the problem of the bad data (BD) in the Phasor Measurement Unit (PMU)-based power systems synchronization, the paper focuses on developing a novel nonlinear estimation framework to track the voltage magnitude, phase angle, and frequency of the utility power grid. Instead of directly analyzing in abc coordinate frame, the symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. By using Clarke's transformation, the sequence networks are then transformed into the alpha beta stationary coordinate frame, and the number of system state variables is reduced to five. A novel Fault Tolerant Extended Kalman Filter real time estimation framework is proposed for power grid system synchronization with bad data measurements. Computer simulation studies show that the novel Fault Tolerant Extended Kalman Filter (FTEKF) provides more accurate results in voltage synchronization, comparing with the Extended Kalman Filter (EKF). The proposed approach has been implemented with dSPACE and CompactRIO hardware in real-time. Our results have shown the potential applications of FTEKF in the future smart power grid synchronization.