Data-Driven Predictive Control for Wide-Area Power Oscillation Damping

TL;DR

This paper explores data-driven predictive control methods, including ARX-based and DeePC, for damping inter-area oscillations in power systems with VSC-HVDC links, demonstrating effective damping with low computational delay.

Contribution

It introduces and compares ARX-based predictive control and DeePC for power oscillation damping, highlighting their efficiency and practicality over traditional model-based controllers.

Findings

ARX-based predictive control effectively damps oscillations.

DeePC achieves comparable damping performance.

ARX methods require less online computation, enabling faster control actions.

Abstract

We study damping of inter-area oscillations in transmission grids using voltage-source-converter-based high-voltage direct-current (VSC-HVDC) links. Conventional power oscillation damping controllers rely on system models that are difficult to obtain in practice. Data-driven Predictive Control (DPC) addresses this limitation by replacing explicit models with data. We apply AutoRegressive with eXogenous inputs (ARX)-based predictive control and its Transient Predictive Control (TPC) variant, and compare them with Data-enabled Predictive Control (DeePC) and two standard model-based controllers. The methods are evaluated in simulation on a system exhibiting both inter-area and local oscillation modes. ARX-based predictive control and DeePC both achieve effective damping, while the ARX-based methods require less online computation. Using warm-started, pre-factorized operator-splitting solvers, ARX/TPC control actions are computed in less than 1ms. These results demonstrate that DPC is a viable approach for power-system oscillation damping for the given test case.