Data-Driven Grid-Forming Control For Statcoms To Achieve Optimal and Adaptive Synchronization

This paper proposes a data-enabled predictive control (DeePC) strategy for static synchronous compensators (STATCOMs) to achieve self-synchronization and grid-forming (GFM) functionality. By embedding the DeePC controller, the resulting data-driven grid-forming STATCOM (DGF-STATCOM) enables model-free, optimal, and adaptive synchronization without relying on explicit system models or oversized energy storage. The implementation and adaptability of DGF-STATCOM are discussed, demonstrating the superiority of deploying DeePC in STATCOMs. A frequency-domain representation of the synchronization dynamics is then established, from which the small-signal impedance model of the DGF-STATCOM is derived. Building on this model, comparative analysis with grid-following (GFL), grid-forming (GFM), and virtual-synchronous-control (ViSynC) STATCOMs highlights its superior voltage-source characteristics and GFM capability. High-fidelity simulations in both single- and two-converter systems further verify its fast synchronization, as well as strong voltage regulation and support capability.