Power grid transient stabilization using Koopman model predictive control

TL;DR

This paper proposes a data-driven Koopman operator-based model predictive control approach to stabilize power grid transients after disturbances, enabling efficient control of complex nonlinear dynamics.

Contribution

It introduces a novel Koopman model predictive control framework for power grid transient stabilization, leveraging data-driven linear predictors for nonlinear systems.

Findings

Successful stabilization of power grid transients after disturbances

Effective use of linear MPC for nonlinear power system dynamics

Demonstrated applicability to wide-area blackout scenarios

Abstract

This work addresses the problem of transient stabilization of a power grid, following a destabilizing disturbance. The model considered is the cascade interconnection of seven New England test models with the disturbance (e.g., a powerline failure) occurring in the first grid and propagating forward, emulating a wide-area blackout. We consider a data-driven control framework based on the Koopman operator theory, where a linear predictor, evolving on a higher dimensional (embedded) state-space, is built from observed data and subsequently used within a model predictive control (MPC) framework, allowing for the use of efficient computational tools of linear MPC to control this highly nonlinear dynamical system.