Online deployment of pulsed power load (PPL) is one of the most challenging issues in DC shipboard integrated power systems (SIPSs), which leads to a multi-objective optimal control problem subject to various constraints in this paper. Since traditional model-based methods face difficulties in designing the optimal control policy and are prone to model inaccuracy and parameter uncertainty, there is an urgent need for a model-free and also high-performance control approach. Thus, a deep reinforcement learning (DRL) optimal control, which employs the twin-delayed deep deterministic policy gradient (TD3) algorithm, is presented in this paper. The DRL optimal control adopts a stack-based state observation technique to enhance learning and control performance, and it uses a multi-objective reward function design to signify the overall dynamic performance. Besides achieving the safe and fast online deployment of PPL, it also fulfills the regulation of DC bus voltage and the proportional current sharing among distributed generations (DGs). Moreover, the DRL control has an advantage in handling the ramp rate constraints of SIPS. The optimal control satisfying ramp rate constraints can be obtained through a deep learning process. The performance of the proposed DRL control is validated by case studies considering different load conditions.

中文翻译：

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通过幻觉原始特征进行无参考图像质量评估


脉冲功率负载（PPL）的在线部署是直流舰载综合电力系统（SIPS）中最具挑战性的问题之一，这导致了本文中受各种约束的多目标最优控制问题。由于传统的基于模型的方法在设计最优控制策略时面临困难，并且容易出现模型不准确和参数不确定的问题，因此迫切需要一种无模型且高性能的控制方法。因此，本文提出了一种采用双延迟深度确定性策略梯度（TD3）算法的深度强化学习（DRL）最优控制。 DRL最优控制采用基于堆栈的状态观察技术来增强学习和控制性能，并使用多目标奖励函数设计来表征整体动态性能。除了实现PPL的安全快速在线部署外，还实现了直流母线电压的调节以及分布式发电（DG）之间的比例均流。此外，DRL 控制在处理 SIPS 的斜坡速率约束方面具有优势。满足斜坡率约束的最优控制可以通过深度学习过程获得。所提出的 DRL 控制的性能通过考虑不同负载条件的案例研究进行了验证。