论文标题
电动传输电压控制的深度加固学习
Deep Reinforcement Learning for Electric Transmission Voltage Control
论文作者
论文摘要
如今，人类操作员主要对电动传输系统进行电压控制。随着电网的复杂性的增加，其操作也是如此，表明额外的自动化可能是有益的。机器学习的一部分被称为“深钢筋学习”（DRL）最近在执行人类通常执行的任务方面表现出了希望。本文将DRL应用于传输电压控制问题，为电压控制提供了开源DRL环境，提出了对“深Q网络”（DQN）算法的新颖修改，并使用最高500辆公交的系统进行大规模执行实验。证明了将DRL应用于电压控制的希望，尽管需要更多的研究来使基于DRL的技术持续超过常规方法。
Today, human operators primarily perform voltage control of the electric transmission system. As the complexity of the grid increases, so does its operation, suggesting additional automation could be beneficial. A subset of machine learning known as deep reinforcement learning (DRL) has recently shown promise in performing tasks typically performed by humans. This paper applies DRL to the transmission voltage control problem, presents open-source DRL environments for voltage control, proposes a novel modification to the "deep Q network" (DQN) algorithm, and performs experiments at scale with systems up to 500 buses. The promise of applying DRL to voltage control is demonstrated, though more research is needed to enable DRL-based techniques to consistently outperform conventional methods.
