Data-Driven Participation Factors for Nonlinear Systems Based on Koopman Mode Decomposition
For power system engineers, it provides a fast, real-time compatible technique to analyze nonlinear oscillations, though it is an incremental extension of existing Koopman-based methods.
This paper introduces a data-driven method for computing participation factors in nonlinear systems using Koopman mode decomposition, generalizing linear definitions. Numerical examples on a canonical system and a two-area four-machine power system demonstrate its effectiveness.
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.