A Hybrid Framework Combining Model-Based and Data-Driven Methods for Hierarchical Decentralized Robust Dynamic State Estimation: Preprint

This paper combines model-based and data-driven methods to develop a hierarchical decentralized robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level is comprised of robust model-based decentralized DSEs. The state estimates sent from the lower level are received at the upper level, where they are filtered by a robust data- driven DSE, after a principled sparse selection. This selection allows us to shrink the dimension of the problem at the upper level, and hence to speed up the computational time significantly. The proposed hybrid framework does not depend upon the centralized infrastructure of the control centers, thus it can be completely embedded into the wide-area measurement systems. This feature will ultimately facilitate the placement of hierarchical decentralized control schemes at the phasor data concentrator (PDC) locations. Also, the network model is not necessary, thus a topology processor is not required. Finally, there is no assumption on the dynamics of the electric loads. The proposed framework is tested on a 2000-bus network.