论文标题
通过深厚的增强学习，降压DC-DC转换器的智能控制策略
An Intelligent Control Strategy for buck DC-DC Converter via Deep Reinforcement Learning
论文作者
论文摘要
作为典型的开关电源，DC-DC转换器已被广泛应用于DC微电网。由于可再生能源产生的变化，具有出色动态特征的DC-DC转换器控制算法的研究和设计具有显着的理论和实际应用值。为了减轻DC Microgrid中的总线电压稳定性问题，首次构建了具有恒定功率载荷（CPLS）的Buck DC-DC转换器的创新智能控制策略，这是第一次构建通过深钢筋学习算法。在本文中，为DC-DC转换器定义了Markov决策过程（MDP）模型和DEEP Q网络（DQN）算法。适当设计的基于无模型的深钢筋学习（DRL）控制策略，以通过奖励/惩罚机制来调整代理 - 环境的相互作用，以实现融合到标称电压。代理通过在没有任何先验知识的情况下提取复杂功率系统的高维特征来做出近似决策。最终，模拟比较结果表明，在不同情况下，提出的控制器具有更强的自学习和自我优化能力。
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.
