Comparison of PI, Fuzzy Logic, and ANFIS Controllers for Power Quality Enhancement in Multi-Mode EV Charging Station

Abstract

Maintaining a stable and clean current profile is an important thing for modern electric vehicle (EV) charging stations that combine renewable energy with the grid. Power electronic converters can introduce harmonic distortions, which affects the overall power quality of the system. To address this issue, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is proposed for the grid-connected inverter. The complete system model was developed and simulated using MATLAB/Simulink. The performance of the proposed ANFIS controller was compared with Proportional-Integral (PI) and Fuzzy Logic Controller (FLC) methods under different solar irradiance conditions. The simulation results show that the proposed ANFIS controller provides better harmonic reduction and better current quality under changing solar conditions. The controller adapts to variations in solar power and load conditions while maintaining stable grid operation. Overall, the results demonstrate that ANFIS is a suitable control approach for improving power quality in grid-connected solar EV charging systems.