Controlling and Performance Evaluation of DC Microgrid Using Adaptive Control Integrated with Blue Whale Optimization

This paper introduces a new method for regulating and assessing DC microgrid performance: adaptive control combined with the Blue Whale Optimization (BWO) algorithm. Improving DC microgrid efficiency, stability, and dependability under a range of load and generation scenarios is the goal. The adaptive control technique responds to microgrid variations by dynamically adjusting control settings in real-time. These control settings are optimized using the BWO algorithm, which is based on the foraging behavior of blue whales and guarantees optimal system performance. Numerous simulations were run in order to evaluate the suggested system's efficacy. Comparing the results to conventional control approaches, it is clear that there have been significant gains in voltage regulation, power quality, and overall system efficiency. Adaptive control combined with BWO significantly reduces disturbances and increases the microgrid's resistance to errors and variations. This methodology exhibits exceptional flexibility and resilience in handling the varied and sporadic characteristics of sustainable energy resources inside the microgrid. By providing a bio-inspired optimization strategy, the findings expand DC microgrid control methodologies. This work highlights the possibility of using evolutionary algorithms in conjunction with adaptive control to attain the best possible operational performance in contemporary energy systems. The suggested approach paves the way for further study on intelligent control strategies in smart grid applications while also improving the operational reliability of DC microgrids.