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Center for Nonlinear Studies |
Since power systems are becoming increasingly complex and subject to more disturbances, developing methods for state estimation and system identification is essential for increasing the reliability of the power grid. Currently this problem is solved on slower, steady state, time scales; however, with the deployment of phasor measurement units (PMUs) throughout the system faster estimation is now possible. Many methods have been studied for dynamic state estimation including both local and global filtering methods. One of the main challenges for global methods is how the filter integrates the differential algebraic equations (DAE) that describe the power system. This work applies implicit methods used to solve DAEs to improve the performance and robustness of an Extended Kalman Filter, making it an attractive state estimation choice. We then explore the performance and robustness of these methods with respect to the number of PMUs, sensor noise, process noise, and disturbances. Further we introduce techniques to reduce the effect of temporary disturbances on the state estimated to help track the state through these faults where the network model is no longer accurate. This is done by switching to a local model. The overall goal of this work is for this state estimation to serve as the basis for a fast estimation layered architecture that that integrates state estimation, change point detection, and classification of disturbances.
Host: Michael Chertkov