Seamless grid following and grid forming inverter mode transition enabled by machine-learning based SCR estimation and smooth switching control.

The increasing penetration of inverter-based resources has led to converter-dominated power systems in which grid strength varies significantly and strongly influences inverter control performance. Grid-following (GFL) and grid-forming (GFM) control strategies offer complementary advantages; however, inappropriate mode selection or abrupt transitions between these modes can result in power oscillations, frequency deviations, and degraded stability, particularly in weak grids. Reliable real-time awareness of grid strength is therefore essential to enable seamless hybrid GFL/GFM operation. This paper proposes a control-oriented, data-driven short-circuit ratio (SCR) estimation framework to support adaptive GFL/GFM mode transitions in grid-connected inverters. A lightweight multilayer perceptron (MLP) is employed to infer grid strength directly from locally measured point-of-common-coupling voltage and current signals, without relying on explicit grid models or intrusive signal injection. The estimated SCR is used as a supervisory signal to select the appropriate operating mode, while parallel GFL and GFM controllers with internal-state synchronization ensure smooth transitions with continuity of phase angle and current references. Comprehensive simulation studies under gradual and abrupt grid-strength variations demonstrate that the proposed approach significantly improves mode-transition behavior compared with conventional ordinary least squares-based estimation. In particular, active and reactive power oscillations are reduced and frequency stability is enhanced during GFL/GFM switching. The results highlight the importance of control-enabling grid-strength estimation for reliable operation of inverter-based resources in weak and converter-dominated power systems.