自适应神经网络教我们什么关于电网
What adaptive neuronal networks teach us about power grids
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。在过去的十年里，对这些看似无关的动态网络类型的研究引起了越来越多的关注。在本文中，我们提供了洞察这两种类型的网络之间的基本关系。为此，我们考虑基于相位振荡器的成熟模型，并显示它们的密切关系。特别是，我们证明了相位振荡器模型的惯性可以被看作是一类特殊的自适应网络。这种关系甚至适用于包括电压动态的更一般类别的电网模型。作为这种关系的直接结果，我们发现了一种新的类型的多集群状态的相位振荡器的惯性。此外，在电网中的连锁线路故障的现象被转化为一个自适应神经网络。
英文摘要：
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.
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