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

TL;DR

This paper introduces a data-driven method using Koopman mode decomposition to compute participation factors in nonlinear systems, enabling analysis of oscillations in complex systems with improved speed and real-time applicability.

Contribution

It extends the concept of participation factors to nonlinear systems through Koopman mode decomposition, providing a practical and efficient computational approach.

Findings

Successfully applied to nonlinear dynamical systems and power systems.

Capable of handling large classes of nonlinearities.

Fast computation suitable for real-time applications.

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.