Enhancing Power Quality in Renewable Energy-Based Distributed Generation Systems through ANFIS-Tuned Unified Power Quality Conditioner (UPQC)

With more and more renewable energy sources being integrated into power distribution networks, it is more important than ever to keep voltage stability and power quality at a high standard. In order to solve the problems with power quality in Distributed Generation (DG) systems, this study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) governed Unified Power Quality Conditioner (UPQC). The study aims to bridge the gap in existing control strategies, which often struggle to maintain stable grid operation during renewable energy fluctuations and load disturbances. The suggested ANFIS-tuned UPQC system improves power quality overall and dynamically regulates voltage and current harmonics, two of the aforementioned problems. The ANFIS-tuned UPQC is compared to both traditional systems and other control approaches in this study, which makes use of modern simulation tools. In particular, the results show that voltage regulation and reduction of total harmonic distortion (THD) are significantly improved, even under circumstances of fluctuating load and grid faults. The findings underscore the effectiveness of the ANFIS-tuned UPQC in optimizing renewable energy integration, making it a robust solution for future power grids. This research presents an innovative approach to enhancing power quality, with potential applications in improving the efficiency and stability of renewable energy-based distribution systems.