Genetic-Algorithm-Driven Parameter Optimization of Three Representative DAB Controllers for Voltage Stability

In the process of integrating renewable energy sources into DC microgrids, isolated bidirectional bridge plays a crucial role. Under load disturbances, voltage fluctuations in microgrid can affect system stability. This study focuses on using Genetic Algorithm to optimize parameters three typical DAB controllers (PI controller based pole placement, sliding mode controller, and model predictive controller) with aim improving stability, especially during sudden drops. The results demonstrate that optimized outperform methods placement traditional manual tuning significantly. For PI maximum drop rate reduced from 8.00% 4.00%. phase margin increased 123° 126°. case decreased 7.50% 5.00%. 127° 155°. As for 1.00% 0.70%. gain 25.8 dB 26.2 dB. These highlight potential optimizing control parameters, offering prospect performance stability DC–DC converters.