Direct Adaptive Control of Grid-Connected Power Converters via Output-Feedback Data-Enabled Policy Optimization

TL;DR

This paper introduces a novel data-driven adaptive control method for stabilizing grid-connected power converters using only online input-output data, enhancing stability and adaptability in complex power systems.

Contribution

It reformulates the control problem as a state-feedback LQR with a non-minimal state and proposes a data-enabled policy optimization method for effective output-feedback control.

Findings

Successfully stabilizes power converters in simulations

Adapts quickly to grid changes

Demonstrates effectiveness of DeePO in control tasks

Abstract

Power electronic converters are becoming the main components of modern power systems due to the increasing integration of renewable energy sources. However, power converters may become unstable when interacting with the complex and time-varying power grid. In this paper, we propose an adaptive data-driven control method to stabilize power converters by using only online input-output data. Our contributions are threefold. First, we reformulate the output-feedback control problem as a state-feedback linear quadratic regulator (LQR) problem with a controllable non-minimal state, which can be constructed from past input-output signals. Second, we propose a data-enabled policy optimization (DeePO) method for this non-minimal realization to achieve efficient output-feedback adaptive control. Third, we use high-fidelity simulations to verify that the output-feedback DeePO can effectively stabilize grid-connected power converters and quickly adapt to the changes in the power grid.