@misc{da6330ba1da240109f0dce47b4510d2d,
title = "Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation",
abstract = "Model-based and data-driven methods are combined to develop a hierarchical decentralized, robust dynamic state estimator (DSE). A two-level hierarchy is proposed, where the lower level consists 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. The proposed hybrid framework does not depend on 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 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 the 2,000-bus synthetic Texas system and shown to be capable of reconstructing the dynamic states of the generators with high accuracy, and of forecasting in the advent of missing data.",
keywords = "data-driven dynamical systems, dynamic state estimation, Kalman filtering, Koopman mode decomposition, Koopman operator",
author = "Marcos Netto and Venkat Krishnan and Yingchen Zhang and Lamine Mili and Yoshihiko Susuki",
year = "2019",
language = "American English",
series = "Presented at Operator Theoretic Methods in Dynamic Data Analysis and Control, 11-15 February 2019, Los Angeles, California",
type = "Other",
}