Power System Dynamic Estimation

Because fast dynamics are becoming more important for power systems, developing methods for dynamic state estimation and system identification is essential for increasing the grid's reliability. This problem has been solved on steady-state time scales, but with the deployment of phasor measurement units (PMUs), faster estimation is now possible. To do this fast estimation we develop a layered architecture that integrates state estimation, change-point detection and disturbance classification. By thinking of these estimation algorithms along with controls as a layered system, it improves our ability to design optimal architectures that are both fast and flexible. State estimation can be achieved using Kalman filtering and particle-based techniques, which assume a system topology and dynamics model. These techniques are adapted to the differential algebraic equations that describe the power system and their robustness is explored. Using these estimates we can make predictions of the future outputs, which then are compared to the PMU data to identify unexpected deviations. These change points then trigger a topology change classifier to identify the new topology of the system after a fault and also triggers a fault tracker to track the state through faults that are cleared. Finally, questions of general architecture design are raised, such as how to optimally link these estimation modules and optimally place sensors to achieve all these objectives.