Application of the Koopman Operator-Theoretic Framework to Power System Dynamic State Estimation

Marcos Netto, Venkat Krishnan, Yingchen Zhang, Lamine Mili, Yoshihiko Susuki

Research output: NRELPoster


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.
Original languageAmerican English
StatePublished - 2019

Publication series

NamePresented at Operator Theoretic Methods in Dynamic Data Analysis and Control, 11-15 February 2019, Los Angeles, California

NREL Publication Number

  • NREL/PO-5D00-73236


  • data-driven dynamical systems
  • dynamic state estimation
  • Kalman filtering
  • Koopman mode decomposition
  • Koopman operator


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