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

Modern power systems that use a lot of renewable energy need grid‑connected inverters that can adjust quickly to changes in grid strength. This framework helps you build a control strategy that switches smoothly between grid‑following and grid‑forming inverter modes. The key idea is to use a machine‑learning estimator to calculate the grid’s Short‑Circuit Ratio (SCR) in real time. Knowing the SCR tells the controller whether the grid is weak or strong, so it can pick the best operating mode on the fly. To avoid sudden overcurrents and voltage spikes when changing modes, the framework includes a smooth‑switching control block. A simulation model lets you test how the system reacts to

transients, how well it tracks active and reactive power, and how stable the frequency stays under different grid‑impedance conditions. The project focuses on: – Organizing the control algorithms – Designing the machine‑learning SCR estimator – Checking the mode‑transition behavior with MATLAB/Simulink or real‑time digital simulators The result is a solid research tool for improving the stability and reliability of modern microgrids and weak distribution networks.

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