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.