Grid Forming Inverter Based on Luenberger Observer Reinforcement Learning for Voltage Control

The rapid integration of renewable energy sources into power grids has introduced significant challenges, particularly in maintaining grid stability and regulating voltage in highly dynamic and unpredictable environments. This research proposes a novel grid-forming inverter (GFIs) design that combines reinforcement learning (RL) and a Luenberger observer (LO). The method uses RL’s flexibility and predictive power to handle dynamic grid conditions, such as fluctuating loads, the incorporation of renewable energy, and disturbance. To improve real-time voltage, estimate and allow for accurate voltage management and synchronization with grid characteristics, the Luenberger observer is utilized. A mathematical model was formulated for RL then combined with LO which was then used to connect the output of the GFIs. When compared to conventional techniques, simulation findings show increased disturbance rejection, quicker response times, and better voltage stability. This creative combination opens the door for the more dependable and effective integration of renewable energy sources into contemporary power systems.