NEURO-FUZZY CONTROLLED DPFC FOR PERFORMANCE ENHANCEMENT OF GRID-INTERFACED HYBRID SYSTEMS

Abstract

The increasing penetration of grid-interfaced hybrid renewable energy systems introduces significant challenges related to power quality, voltage regulation, and dynamic stability. Distributed Power Flow Controllers (DPFCs), derived from Unified Power Flow Controller (UPFC) topology, offer flexible power flow control with reduced cost and enhanced reliability. However, conventional PI-based control strategies exhibit poor adaptability under system nonlinearities and varying operating conditions. This paper proposes an Neuro-Fuzzy Inference System (NEURO FUZZY)–controlled DPFC to enhance the overall performance of grid-interfaced hybrid energy systems. The proposed controller dynamically regulates series and shunt converter operations to improve voltage stability, power flow control, and harmonic mitigation. Detailed modeling of the hybrid system and DPFC is carried out in MATLAB/Simulink. Simulation results demonstrate superior performance of the proposed NEURO FUZZY-DPFC in terms of reduced Total Harmonic Distortion (THD), improved voltage regulation, faster dynamic response, and enhanced system stability when compared with conventional PI and fuzzy logic controllers.