论文标题
在电力系统中进行连续鲁棒性控制问题的对抗训练
Adversarial Training for a Continuous Robustness Control Problem in Power Systems
论文作者
论文摘要
我们提出了一种新的对抗训练方法，用于在为即将到来的网络物理动力系统设计控制器时注入鲁棒性。以前的方法深深依赖模拟无法应付上升的复杂性，并且在计算预算上在线使用时的成本过高。相比之下，我们的方法在显示有用的鲁棒性属性的同时被证明是计算上有效的。为此，我们对对抗性框架进行建模，建议实施固定对手策略，并在L2RPN（学习运行电源网络）环境上进行测试。该环境是一个构成IEEE 118网格的三分之一的网络物理系统的合成但现实的建模。使用对抗性测试，我们分析了从L2RPN竞争的稳健性轨道中提交的训练剂的结果。然后，我们通过量身定制的评估指标进一步评估这些代理在连续N-1问题方面的性能。我们发现，一些以对抗性方式训练的代理商在这方面表现出有趣的预防行为，我们将讨论。
We propose a new adversarial training approach for injecting robustness when designing controllers for upcoming cyber-physical power systems. Previous approaches relying deeply on simulations are not able to cope with the rising complexity and are too costly when used online in terms of computation budget. In comparison, our method proves to be computationally efficient online while displaying useful robustness properties. To do so we model an adversarial framework, propose the implementation of a fixed opponent policy and test it on a L2RPN (Learning to Run a Power Network) environment. This environment is a synthetic but realistic modeling of a cyber-physical system accounting for one third of the IEEE 118 grid. Using adversarial testing, we analyze the results of submitted trained agents from the robustness track of the L2RPN competition. We then further assess the performance of these agents in regards to the continuous N-1 problem through tailored evaluation metrics. We discover that some agents trained in an adversarial way demonstrate interesting preventive behaviors in that regard, which we discuss.
