论文标题
哪些自适应神经元网络教给我们有关电网的知识
What adaptive neuronal networks teach us about power grids
论文作者
论文摘要
电网网络以及具有突触可塑性的神经元网络，描述了对我们日常生活非常重要的现实世界系统。在过去的十年中，对这些看似无关的动态网络类型的调查引起了人们的关注。在本文中，我们提供了有关这两种类型网络之间基本关系的洞察力。为此，我们考虑基于相位振荡器的公认模型，并显示其亲密关系。特别是，我们证明具有惯性的相位振荡器模型可以视为特定类别的自适应网络。这种关系即使对于包括电压动力学的更通用的电网模型的类别也存在。作为这种关系的直接结果，我们发现了一种新型的惯性相位振荡器的多簇状态。此外，功率网格中级联线故障的现象被转化为自适应神经元网络。
Power grid networks, as well as neuronal networks with synaptic plasticity, describe real-world systems of tremendous importance for our daily life. The investigation of these seemingly unrelated types of dynamical networks has attracted increasing attention over the last decade. In this paper, we provide insight into the fundamental relation between these two types of networks. For this, we consider well-established models based on phase oscillators and show their intimate relation. In particular, we prove that phase oscillator models with inertia can be viewed as a particular class of adaptive networks. This relation holds even for more general classes of power grid models that include voltage dynamics. As an immediate consequence of this relation, we find a novel type of multicluster state for phase oscillators with inertia. Moreover, the phenomenon of cascading line failure in power grids is translated into an adaptive neuronal network.
