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

Abstract

This paper 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.

References in corpus (1)

• Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control

Cited by in corpus (5)

• On analytical construction of observable functions in extended dynamic mode decomposition for nonlinear estimation and prediction
• Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model
• A mode-in-state contribution factor based on Koopman operator and its application to power system analysis
• Participation Factors for Nonlinear Autonomous Dynamical Systems in the Koopman Operator Framework
• Statistical analysis and method to quantify the impact of measurement uncertainty on dynamic mode decomposition