Neuro-fuzzy-SVPWM switched-inductor-capacitor-based boost inverter for grid-tied fuel cell power generators, design and implementation

Abstract

Hydrogen energy shows promise as a renewable energy source for various applications like battery and electric vehicle charging stations, as well as grid connections. However, high current ripple from fuel cells (FCs) and inadequate voltages for grid use pose challenges. This study presents a novel solution using neural fuzzy network control in a high-gain DC-DC boost converter to address these issues. The suggested converter charges in parallel and discharges in series, minimizing the current ripple range in the fuel cell network. Additionally, the switchcapacitor cell efficiently increases the output voltage. In this study, a Neuro-fuzzy system with 9 rules is trained meticulously over 50 epochs using hybrid optimization and grid partition methods, achieving a low training error of 0.045 with 522,064 samples. The neural fuzzy network, employing the weighted average method for Defuzzification, produces duty cycle values from 0.02 to 0.5 in response to input signals. Additionally, an innovative Space Vector Pulse Width Modulation (SVPWM) approach within the inverter circuit enhances voltage generation precision and power quality for grid delivery, notably reducing current ripple and ensuring stable power supply. This combined with the neural fuzzy network in the converter efficiently converts hydrogen energy into AC voltage for seamless grid integration, revolutionizing boost converter efficiency and advancing hydrogen energy utilization across various energy sectors.