# Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

## TL;DR

This paper introduces a data-driven method using Koopman operators to identify and predict nonlinear power system dynamics, explicitly handling measurement noise and demonstrating effectiveness on a standard test system.

## Contribution

It presents a robust Koopman operator-based framework for modeling power system dynamics directly from noisy data, advancing the state-of-the-art in data-driven power system analysis.

## Key findings

- Effective identification of nonlinear dynamics from noisy data
- Accurate prediction of power system state trajectories
- Successful application to IEEE nine bus test system

## Abstract

In this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.