A Novel Finite Control Set Model Predictive Control for Transformer Energization in Black Start

Inverter-based resources (IBRs) are essential for rapid microgrid restoration following blackouts, as they can provide black start services. However, transformer energization during a black start generates a magnetizing inrush that destabilizes the inverter, often leading to failure in grid restoration. This phenomenon represents a significant challenge for the effective deployment of IBRs in black start operations. To address this issue, this paper proposes a novel finite control set model predictive control (FCS-MPC) approach for inverter-based transformer energization aimed at enabling successful microgrid black start. The paper begins with an analysis of the flux characteristics of the transformer. Then, an autoregressive with exogenous input (ARX) model is developed to predict the transformer’s magnetizing current. A magnetizing inrush mitigation algorithm is then introduced, combining the ARX model with a classical two-level inverter voltage control strategy. Comparative simulations and hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed method, demonstrating significant improvements in mitigating magnetizing inrush, achieving fast dynamic responses, and maintaining stable static waveforms.