Data-Driven Linear Koopman Embedding for Networked Systems: Model-Predictive Grid Control

TL;DR

This paper introduces a data-driven, scalable Koopman embedding method using deep neural networks for real-time model predictive control of power grid voltage, eliminating the need for predefined basis functions.

Contribution

It proposes a novel KDNN architecture that learns a linear embedding of nonlinear networked dynamics directly from data, enabling efficient real-time control without ad-hoc basis selection.

Findings

Effective voltage control in IEEE 39-bus system

Robustness to system variations

Scalable real-time implementation

Abstract

This paper presents a data-learned linear Koopman embedding of nonlinear networked dynamics and uses it to enable real-time model predictive emergency voltage control in a power network. The approach involves a novel data-driven ``basis-dictionary free" lifting of the system dynamics into a higher dimensional linear space over which an MPC (model predictive control) is exercised, making it both scalable and rapid for practical real-time implementation. A Koopman-inspired deep neural network (KDNN) encoder-decoder architecture for the linear embedding of the underlying dynamics under distributed controls is presented, in which the end-to-end components of the KDNN comprising of a triple of transforms is learned from the system trajectory data in one go: A Neural Network (NN)-based lifting to a higher dimension, a linear dynamics within that higher dimension, and an NN-based projection to the original space. This data-learned approach relieves the burden of the ad-hoc selection of the nonlinear basis functions (e.g., polynomial or radial) used in conventional approaches for lifting to higher dimensional linear space. We validate the efficacy and robustness of the approach via application to the standard IEEE 39-bus system.