Abstract
DC‐DC converters are essential for integrating distributed energy resources into microgrid (MG) systems. These converters are designed to incorporate intermittent renewable energy sources such as solar photovoltaic (PV) panels, fuel cells (FCs), and battery energy storage systems (BESSs) into the grid. However, conventional DC‐DC converters have limitations including lower efficiency, voltage ripple, insufficient voltage regulation, and compatibility issues. This article presents boost and bidirectional buck‐boost converters for direct current microgrid (DCMG) applications, employing an adaptive neuro‐fuzzy inference system (ANFIS) for control. These proposed converter configurations adeptly manage wide input voltage fluctuations from intermittent sources, consistently supplying power to the DC bus at 500 V and 120 V for boost and buck operations, respectively, with an efficiency of 98.8%. The output voltage result shows that the ANFIS‐based boost converter has 10% overshoot as compared to 41% and 50% overshoot in proportional integral (PI) and fuzzy logic controller (FLC), respectively. In both buck and boost modes, the converters’ voltage gain is influenced by duty ratio adjustments only, not sensitive to dynamic input voltage and flexible manipulation of the output voltage for BESS charging. Moreover, the designed converters accommodate load variations within the MG. To assess the converters’ ability to regulate output voltage effectively, PI, FLC, and ANFIS controllers are implemented and compared. And the ANFIS controller demonstrates superior performance, offering faster response times and enhanced stability. Evaluations are conducted through simulations in the MATLAB/Simulink environment.
Funder
Institute for Basic Sciences, technology and Innovation, Pan African University