Domain Adaptation-Based Transfer Learning for DRL Control Implementation of DC Microgrids

In response to the voltage fluctuations and non-proportional current distribution caused by the negative impedance characteristics of constant power loads (CPLs) and the high penetration of renewable energy in dc microgrids, this article presents a novel model-free control approach leveraging transfer learning. Unlike traditional model-based methods that require accurate system models, this model-free strategy enhances system stability and adaptability under varying operational conditions. By utilizing deep reinforcement learning (DRL), the agent interacts with the environment, learning to make decisions that are not reliant on precise models of the system. Furthermore, domain adaptation techniques are employed to improve the control policy’s generalization across diverse scenarios. Experimental results demonstrate that the proposed technique reduces voltage deviation by 33%, suppresses transient peak current by 30%, and provides a 34% faster settling time for voltage regulation and current distribution, while ensuring robust dynamic performance across varying conditions.