Grid integration of solar photovoltaic system using machine learning-based virtual inertia synthetization in synchronverter

In recent years, the domination of power electronics-interfaced renewable energy source (RES) such as solar photovoltaic (PV) system causes grid frequency instability issue. This paper proposes a new machine learning (ML)-based virtual inertia (VI) synthetization in synchronverter topology to integrate the solar PV system and the power grid with high-frequency stability. The proposed ML-based VI is synthetized by amalgamating the action and critic network to decouple active and reactive power control. Therefore, the proposed synchronverter exhibits decoupled control and flexible moment of inertia ( $J$ ) changes that lead to high stability and fast transient response as compared to the conventional proportional-integral (PI) and fuzzy logic (FL)-based synchronverters. Various case studies in MATLAB/ Simulink simulation have been carried out, and the results proved the feasibility and effectiveness of the proposed ML-based synchronverter. Through the proposed control strategy, the maximum frequency deviation from the nominal value, settling time to reach quasi-steady-state frequency and steady-state error has been reduced by 0.1Hz, 35% and 27% respectively.