Adaptive Neuro-Fuzzy Controlled UPQC for Power Quality Enhancement in Renewable Energy Integrated Distributed Generation System

The growing integration of sustainable power sources into contemporary power distribution systems introduces various PQ issues, as well as, harmonic distortion, voltage fluctuations and reactive power imbalance. To mitigate these problems, in this paper ANFIS controlled UPQC. The UPQC for a DG network that integrates renewable energy is presented. The proposed UPQC- ANFIS architecture is implemented in a three-phase low-voltage hybrid system combining wind energy and PV sources. The ANFIS controller combines the power of artificial neural networks with fuzzy logic for more efficient reasoning and faster dynamic reaction, even when the load and source circumstances are changing. It also ensures precise compensation. The UPQC provides effective mitigation of voltage sags, swells, and current harmonics, while maintaining near-unity power factor and facilitating real power injection into the grid. The performance of system is analyzed through detailed MATLAB/Simulink simulations under dynamic and steady state conditions. The results demonstrate that the ANFIS- based UPQC exhibits superior voltage regulation, enhanced response of transient, and significantly reduced Total Harmonic Distortion compared to the conventional ANN-controlled UPQC, validating its capability to enhance overall power quality in renewable energy-based DG networks.