# Data-Enabled Predictive Control for Grid-Connected Power Converters

## TL;DR

This paper introduces a data-driven control approach for grid-connected power converters, combining DeePC and ARMA-based MPC to improve stability and performance without relying on explicit system models.

## Contribution

It presents a novel combination of DeePC and ARMA-based MPC methods, addressing scalability issues and demonstrating effectiveness in stabilizing power converters.

## Key findings

- DeePC eliminates undesired oscillations in power converters.
- ARMA-based MPC improves scalability for high-order systems.
- The combined approach stabilizes a voltage source converter in a power system.

## Abstract

We apply a novel data-enabled predictive control (DeePC) algorithm in grid-connected power converters to perform safe and optimal control. Rather than a model, the DeePC algorithm solely needs input/output data measured from the unknown system to predict future trajectories. We show that the DeePC can eliminate undesired oscillations in a grid-connected power converter and stabilize an unstable system. However, the DeePC algorithm may suffer from poor scalability when applied in high-order systems. To this end, we present a finite-horizon output-based model predictive control (MPC) for grid-connected power converters, which uses an N-step auto-regressive-moving-average (ARMA) model for system representation. The ARMA model is identified via an N-step prediction error method (PEM) in a recursive way. We investigate the connection between the DeePC and the concatenated PEM-MPC method, and then analytically and numerically compare their closed-loop performance. Moreover, the PEM-MPC is applied in a voltage source converter based HVDC station which is connected to a two-area power system so as to eliminate low-frequency oscillations. All of our results are illustrated with high-fidelity, nonlinear, and noisy simulations.