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arXiv · 2601.19638

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

Giacomo Mastroddi·Jan Poland·Mats Larsson·Keith Moffat
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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.

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Giacomo Mastroddi, Jan Poland, Mats Larsson, Keith Moffat. 2026-01-27. Data-Driven Predictive Control for Wide-Area Power Oscillation Damping. https://arxiv.org/abs/2601.19638

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