Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model

Yijun Xu, Marcos Netto, Lamine Mili

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a linear dynamical system model. This transformation allows us to analyze the evolution of the system dynamics through its Koopman eigenfunctions, eigenvalues, and modes. Of particular importance for this letter, the obtained linear dynamical system is a surrogate that enables the evaluation of parameter uncertainties by simply perturbing the initial conditions of the Koopman eigenfunctions associated with the pseudo-state variables. Simulations carried out on the New England test system reveal the excellent performance of the proposed method in terms of accuracy and computational efficiency.

Original languageEnglish (US)
Pages (from-to)3157-3160
Number of pages4
JournalIEEE Transactions on Power Systems
Volume37
Issue number4
DOIs
StatePublished - Jul 1 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Keywords

  • Koopman operator
  • parameter uncertainty
  • statistical dynamic simulation
  • uncertainty propagation

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