论文标题
用于缓解网络攻击的深度加强学习
Deep Reinforcement Learning for DER Cyber-Attack Mitigation
论文作者
论文摘要
设置了DER使用智能互换功能的DER的渗透率增加，以将电气分配网络从被动系统（具有固定注入/消耗）转换为具有数百个分布式控制器的主动网络，该网络会动态调节其操作设定点作为系统条件的函数。通过通过网格代码和/或国际标准对功能的标准化实现了这一过渡。但是，DER是独一无二的，因为它们通常既不是由分销公用事业公司拥有也不是由分销公用事业公司拥有的，因此代表了用于网络物理攻击的新的新兴攻击向量。在这项工作中，我们将深度强化学习视为一种工具，以学习一组不妥协的DER单元的控制逻辑的最佳参数，以积极减轻网络攻击对网络DER子集的影响。
The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.
