Data-Driven Participation Factors for Nonlinear Systems Based on Koopman Mode Decomposition

Marcos Netto, Yoshihiko Susuki, Lamine Mili

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


This letter develops a novel data-driven technique to compute the participation factors for nonlinear systems based on the Koopman mode decomposition. Provided that certain conditions are satisfied, it is shown that the proposed technique generalizes the original definition of the linear mode-in-state participation factors. Two numerical examples are provided to demonstrate the performance of our approach: one relying on a canonical nonlinear dynamical system, and the other based on the two-area four-machine power system. The Koopman mode decomposition is capable of coping with a large class of nonlinearity, thereby making our technique able to deal with oscillations arising in practice due to nonlinearities while being fast to compute and compatible with real-time applications.

Original languageEnglish (US)
Article number8470965
Pages (from-to)198-203
Number of pages6
JournalIEEE Control Systems Letters
Issue number1
StatePublished - Jan 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization


  • Koopman mode decomposition
  • modal analysis
  • modal participation factors
  • nonlinear systems
  • stability


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