Data-driven MPC of descriptor systems: A case study for power networks

TL;DR

This paper demonstrates the application of data-driven predictive control to a discretized power system model, showing its effectiveness and potential for controlling descriptor systems in power networks.

Contribution

It extends data-driven predictive control methods to descriptor systems and provides a practical case study on power network dynamics.

Findings

Effective control of power-swing equations using data-driven MPC

Validation of the framework's applicability to descriptor systems

Potential for improved power network stability control

Abstract

Recently, data-driven predictive control of linear systems has received wide-spread research attention. It hinges on the fundamental lemma by Willems et al. In a previous paper, we have shown how this framework can be applied to predictive control of linear time-invariant descriptor systems. In the present paper, we present a case study wherein we apply data-driven predictive control to a discrete-time descriptor model obtained by discretization of the power-swing equations for a nine-bus system. Our results shows the efficacy of the proposed control scheme and they underpin the prospect of the data-driven framework for control of descriptor systems.