Data-driven decision making for enhancing small-signal stability in hybrid AC/DC grids through converter control role assignment

Hybrid AC/DC transmission grids incorporate High-Voltage Direct-Current links, enabled by the presence of Interconnecting Power Converters (IPCs). The control role assigned to each IPC significantly influences grid dynamics. Traditionally, these converters operate with static control roles, but recent studies have proposed scheduling their roles based on day-ahead forecasts to enhance stability performance. However, in systems with high renewable energy penetration, forecast deviations can render scheduled control assignments suboptimal or even lead to instability. To address this challenge, this work proposes an online scheduling recalculation algorithm that dynamically adapts IPC control roles during system operation. The approach leverages a data-driven multi-criteria decision-making framework, integrating surrogate models of conventional small-signal stability analysis tools to enable a fast computation of system stability and stability performance indicators. • Data-driven MCDM algorithm for IPC control, improving hybrid AC/DC grid stability. • Methodology to train ML surrogate models for fast stability assessment and performance estimation. • Scalablility enhancement via data mining, clustering, and set intersection.