Performance Analysis of FPGA-Based Semi-Dual Active Bridge DC-DC Boost Converter Using Artificial Intelligence Optimization Algorithms

This study presents a FPGA-based design and implementation of a semi-dual active bridge (S-DAB) DC-DC boost converter to raise the DC voltage generated by renewable sources while effectively regulating it against line and load fluctuations for inverter applications. The topology and zero current switching technique of the S-DAB converter play crucial roles in enhancing the overall performance and increasing the efficiency. This study employs optimization algorithms such as the neural network controller (NNC), sliding mode controller (SMC), genetic algorithm (GA), firefly algorithm (FA), and particle swarm optimization (PSO) for effective voltage regulation, fast transient response, and stable steady-state response. The S-DAB converter attained an output voltage of 350 V at a current of 0.92 A from the input voltage of 20 V at a current of 17 A and achieved an efficiency of 94.07%. The S-DAB converter was designed and simulated using MATLAB Simulink, and its performance metrics was measured and analyzed for different optimization algorithms. The hardware prototype of the S-DAB converter was implemented using a FPGA controller with different optimization algorithms to provide better voltage regulation and to enhance the converter's time-domain values. The converter's simulation and experimental results were compared, validated, and analyzed, showing close agreement in performance metrics.