PV, Wind, Battery integrated UPQC system Performance with PI, Fuzzy and ANFIS controllers

This research paper investigates the performance of a Unified Power Quality Conditioner (UPQC) system integrated with photovoltaic (PV) panels, wind energy, and battery storage, using three distinct control strategies: Proportional-Integral (PI), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers. The integration of renewable energy sources with battery storage into the UPQC aims to enhance power quality and system reliability in the presence of grid disturbances such as voltage sags, swells, and harmonics. The study begins by modeling the PV, wind, and battery components, followed by the implementation of the UPQC system. Each controller is then applied to the system, and its performance is evaluated based on metrics such as Total Harmonic Distortion (THD), voltage regulation during disturbances, and the speed of system response. Simulation results indicate that while the PI controller offers satisfactory performance under stable conditions, it struggles with rapid changes in load and supply. In contrast, the Fuzzy Logic controller demonstrates improved adaptability and robustness, particularly in handling non-linearities. However, the ANFIS controller outperforms both PI and Fuzzy controllers by combining the benefits of adaptive learning and fuzzy logic, resulting in superior power quality improvement and faster settling times during grid disturbances. This paper concludes that the ANFIS controller is the most effective in managing the complexities of a PV, wind, and battery-integrated UPQC system, offering significant advantages in both power quality enhancement and system resilience.