Deep reinforcement learning-based controller for DC-link voltage regulation and voltage sag compensation in a solar PV-integrated UPQC system

Power quality problems including dynamic load variations, harmonic distortion, and voltage sags are significant when renewable energy sources, such solar photovoltaic (PV) systems, are integrated into modern distribution networks. These issues are often mitigated by devices known as Unified Power Quality Conditioners (UPQCs), which control both series and shunt power disturbances. However, traditional control systems, such as PQ theory-based PI controllers, usually fail to sustain good performance under quickly changing solar and grid conditions. The proposed work a Deep Reinforcement Learning (DRL) with PI Controller for DC-link voltage regulation and voltage sag correction in a solar PV integrated UPQC system. The DRL model can automatically adapt to changes in voltage and current in real time by learning the most effective compensating strategies. It has a unique reward system that gives priority to low total harmonic distortion (THD), quick voltage recovery, and low power losses. Variable temperature and irradiance conditions are used to model the solar PV system, which is then connected to the grid through the UPQC to provide both linear and non-linear loads. Maintaining DC-link voltage, minimising voltage sags, and guaranteeing clean power delivery are all improved by the proposed DRL-based control framework as compared to the conventional PQ theory-based technique. For a grid-integrated PV-UPQC system, the suggested DRL-PI controller greatly minimises power quality problems, attaining voltage THD of 1.01% and current THD of 1.63% as opposed to 3.13% and 10.64% with PQ-PI control. As compared to 0.95 s (PQ-PI), the DC-link settling time is significantly reduced to 0.25 s. The dynamic and harmonic performance of these results is superior to that of the traditional PQ-PI controller.