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

Feiran Zhao, Ruohan Leng, Linbin Huang, Huanhai Xin, Keyou You, Florian Dörfler

TL;DR

This work addresses the instability risks of grid-connected power converters in unknown, time-varying grids by introducing an output-feedback data-enabled policy optimization (DeePO) framework. By reformulating the output-feedback problem as a state-feedback LQR with a controllable non-minimal state derived from past input-output data, the approach enables direct, online adaptation without explicit system identification. DeePO employs covariance-parameterized LQR design and online gradient updates, with stability guaranteed via a projected gradient and subspace constraints, and a permutation-based reduction to ensure controllability. High-fidelity simulations on grid-connected converters and direct-drive wind generators demonstrate rapid adaptation to grid changes, effective damping of oscillations, and significant computational advantages over DeePC, highlighting practical impact for real-time grid stabilization.

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.

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

TL;DR

This work addresses the instability risks of grid-connected power converters in unknown, time-varying grids by introducing an output-feedback data-enabled policy optimization (DeePO) framework. By reformulating the output-feedback problem as a state-feedback LQR with a controllable non-minimal state derived from past input-output data, the approach enables direct, online adaptation without explicit system identification. DeePO employs covariance-parameterized LQR design and online gradient updates, with stability guaranteed via a projected gradient and subspace constraints, and a permutation-based reduction to ensure controllability. High-fidelity simulations on grid-connected converters and direct-drive wind generators demonstrate rapid adaptation to grid changes, effective damping of oscillations, and significant computational advantages over DeePC, highlighting practical impact for real-time grid stabilization.

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.

Paper Structure

This paper contains 16 sections, 3 theorems, 26 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Stabilization of grid-connected power converters
  3. Power converter systems
  4. Challenges in stabilization of power converter systems (DC source + DC-AC converter + AC power grid)
  5. Our approach
  6. A controllable non-minimal realization of the output-feedback system
  7. A non-minimal controllable state
  8. Computing the permutation matrix from input-output data
  9. Output-feedback data-enabled policy optimization for direct adaptive control
  10. Direct data-driven LQR design of \ref{['sys:reduce']} with covariance parameterization
  11. Output-feedback DeePO
  12. DeePO for stabilization of power converters and renewable energy systems
  13. Implementation of output-feedback DeePO for power converter systems
  14. Simulations on a grid-connected power converter
  15. Simulations on a direct-drive wind generator
  16. ...and 1 more sections

Key Result

Lemma 1

A non-minimal realization of equ:outputsys is given by equ:nonnimimal_realization shown at the bottom of this page.

Figures (5)

  • Figure 1: One-line diagram of a grid-connected power converter. Here the DC side is connected to lithium batteries, while it can also be wind turbines.
  • Figure 2: Time-domain responses of the grid-connected power converter. The DeePO is activated at $t = 3.5\,\mathrm{s}$. --- with DeePO; --- without DeePO.
  • Figure 3: The application of DeePO to stabilize a direct-drive wind generator.
  • Figure 4: The time-domain responses of the wind generator: (a) active power and (b) reactive power. The DeePO is activated at $t = 6.0\,\mathrm{s}$. --- with DeePO; --- without DeePO.
  • Figure 5: The time-domain responses of the wind generator: (a) active power and (b) reactive power. --- with DeePO controller; --- with non-adaptive controller.

Theorems & Definitions (4)

  • Lemma 1: A non-minimal realization
  • proof
  • Lemma 2: A controllable non-minimal realization
  • Lemma 3: zhao2024data