论文标题
基于亚当的增强随机搜索为分布式能源资源网络攻击缓解的控制政策
Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation
论文作者
论文摘要
VAR和伏特控制功能是分布式能源资源（DER）电源逆变器中包含的机制，以减轻分配系统中过高或低电压的机制。如果DED子集的伏特和伏特瓦特设置受到网络攻击的一部分受到损害，我们提出了一种机制，以控制剩余的一组非副不动的DER，以改善系统伏特的大型较大振荡，并实时实时进行大电压不平衡。为此，我们为单个不受影响的DER构建控制策略，直接使用基于ADAM的增强随机搜索（ARS）直接搜索策略空间。在本文中，我们表明，与以前旨在使用深度强化学习（DRL）培训网络安全策略的努力相比，所提出的方法能够学习最佳（有时是线性）策略比传统的DRL技术更快的数量级（例如，近距离策略优化）。
Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems. In the event that a subset of DER have had their Volt-VAR and Volt-Watt settings compromised as part of a cyber-attack, we propose a mechanism to control the remaining set of non-compromised DER to ameliorate large oscillations in system voltages and large voltage imbalances in real time. To do so, we construct control policies for individual non-compromised DER, directly searching the policy space using an Adam-based augmented random search (ARS). In this paper we show that, compared to previous efforts aimed at training policies for DER cybersecurity using deep reinforcement learning (DRL), the proposed approach is able to learn optimal (and sometimes linear) policies an order of magnitude faster than conventional DRL techniques (e.g., Proximal Policy Optimization).
