An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning
This addresses voltage stability issues in DC microgrids for renewable energy applications, but it is incremental as it applies existing DRL methods to a specific domain.
The paper tackled bus voltage stability in DC microgrids with constant power loads by proposing a deep reinforcement learning control strategy for buck DC-DC converters, demonstrating stronger self-learning and optimization capabilities in simulations.
As a typical switching power supply, the DC-DC converter has been widely applied in DC microgrid. Due to the variation of renewable energy generation, research and design of DC-DC converter control algorithm with outstanding dynamic characteristics has significant theoretical and practical application value. To mitigate the bus voltage stability issue in DC microgrid, an innovative intelligent control strategy for buck DC-DC converter with constant power loads (CPLs) via deep reinforcement learning algorithm is constructed for the first time. In this article, a Markov Decision Process (MDP) model and the deep Q network (DQN) algorithm are defined for DC-DC converter. A model-free based deep reinforcement learning (DRL) control strategy is appropriately designed to adjust the agent-environment interaction through the rewards/penalties mechanism towards achieving converge to nominal voltage. The agent makes approximate decisions by extracting the high-dimensional feature of complex power systems without any prior knowledge. Eventually, the simulation comparison results demonstrate that the proposed controller has stronger self-learning and self-optimization capabilities under the different scenarios.