DESIGN AND IMPLEMENTATION OF ANFIS CONTROLLER BASED UPQC WITH RENEWABLE ENERGY BASED DISTRIBUTED GENERATION SYSTEM

This study investigates the performance of a renewable energy-based distributed generation system utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) tuned Unified Power Quality Conditioner (UPQC). The system integrates photovoltaic (PV) and wind energy sources to address power quality issues and enhance the reliability of power distribution networks. The ANFIS-tuned UPQC is designed to dynamically optimize its control strategies, responding effectively to fluctuations in power generation and varying load demands. The main goals of this study are to determine how well the system compensates for reactive power loss and maintains voltage regulation, as well as how well it can reduce common power quality issues including harmonics, voltage sags, and swells. The research shows how well the ANFIS-tuned UPQC performs in providing a reliable and high-quality power supply through extensive simulation and analysis. Key findings indicate that the integration of ANFIS with UPQC not only enhances the power quality but also improves the overall efficiency and resilience of the distributed generation system. The system effectively utilizes renewable energy sources, reducing dependency on conventional power generation and contributing to environmental sustainability. The results highlight the potential of this advanced control strategy in promoting the widespread adoption of distributed generation systems based on renewable energy, ensuring both reliability and sustainability in modern power grids