An Intelligent Deep Reinforcement Learning Control for DC-DC Power Buck Converter Feeding a Constant Power Load

In the DC microgrid, a DC-DC converter has now been widely used as a conventional switching power supply. A suitable control system needs to be developed for a DC-DC converter to obtain the best dynamic response which is very much essential theoretically and in real-world applications since the variability of renewable energy supply is uncertain. A unique intelligent control method is proposed for buck converters to constant power loads (CPLs). In this work, the bus voltage stability issues arising in DC microgrids are addressed using deep reinforcement learning (DRL) by employing a Markov decision process (MDP). To adapt the agent-environment interaction via reward/penalties mechanism for the convergence of nominal bus voltage, a model-free DRL control method is suggested. The high-dimensional characteristic is collected by agent from complex systems which does not require any previous knowledge in order to create approximations. Lastly, the outcomes of the simulation analysis demonstrate that under various conditions, the recommended controller has a higher ability for self-improvement and learning.