Power Quality Improvement and System Stability Enhancement in Grid-Connected PV-Battery System Using ANN Controlled DVR and Shunt Active Power Filter

G Suresh Kumar, J Nagaraju, Kmalesh Kumar

This paper presents an enhanced control strategy for improving power quality and system stability in a low-voltage grid-connected solar photovoltaic (PV) and battery energy storage system. In the proposed system, a Dynamic Voltage Restorer (DVR) is integrated at the grid side to mitigate power quality disturbances such as voltage sag and voltage swell, thereby ensuring reliable and continuous power supply to the connected load. The PV array is connected to the DC bus through a DC–DC boost converter for maximum power extraction, while the battery energy storage system supports bidirectional power flow for charging and discharging operations based on system demand. To achieve intelligent and adaptive control, an Artificial Neural Network (ANN) controller is employed for the DVR operation. The ANN controller accurately detects voltage disturbances and generates appropriate compensation signals in real time, resulting in faster dynamic response and improved disturbance rejection compared with conventional controllers. In addition, the proposed control strategy enhances voltage regulation, reduces harmonic distortion, improves power factor, and maintains DC bus stability under varying grid and load conditions. The complete system is modeled and analyzed using MATLAB Simulink. Simulation results demonstrate effective mitigation of voltage sag and swell, improved load voltage profile, enhanced system stability, and reduction of total harmonic distortion (THD) within the limits specified by the Institute of Electrical and Electronics Engineers IEEE-519 standard. The proposed method provides an efficient and reliable solution for renewable energy integrated smart grid applications.