论文标题
重新考虑基于AI的电网控制：潜入算法设计
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
论文作者
论文摘要
最近，基于深厚的加固学习（DRL）的方法已显示了在动力工程领域中解决复杂的决策和控制问题的承诺。在本文中，我们对基于DRL的电压控制的深入分析，从算法选择，州空间空间表示和奖励工程奖励。解决基于IMITITIMITIT IMITATIT基于学习的方法，而不是直接绘制了互动的动力，以绘制了任何相互作用的操作，以绘制有效的动力。性能结果表明，该方法具有强大的概括能力，而训练时间较少。通过模仿学习训练的代理可以有效且坚固，可以解决伏击控制问题，并且表现优于以前的RL代理。
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering.To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process. The performance results demonstrate that theproposed approach has strong generalization ability with much less training time.The agent trained by imitation learning is effective and robust to solve voltagecontrol problem and outperforms the former RL agents.
