CCSS and CTP, Seoul National University, Seoul 08826, South Korea.
Center for Complex Systems and KI for Grid Modernization, Korea Institute of Energy Technology, Naju, Jeonnam 58217, South Korea.
Chaos. 2023 Jan;33(1):013115. doi: 10.1063/5.0107420.
Cascading failures in electrical power grids, comprising nodes and links, propagate nonlocally. After a local disturbance, successive resultant can be distant from the source. Since avalanche failures can propagate unexpectedly, care must be taken when formulating a mitigation strategy. Herein, we propose a strategy for mitigating such cascading failures. First, to characterize the impact of each node on the avalanche dynamics, we propose a novel measure, that of Avalanche Centrality (AC). Then, based on the ACs, nodes potentially needing reinforcement are identified and selected for mitigation. Compared with heuristic measures, AC has proven to be efficient at reducing avalanche size; however, due to nonlocal propagation, calculating ACs can be computationally burdensome. To resolve this problem, we use a graph neural network (GNN). We begin by training a GNN using a large number of small networks; then, once trained, the GNN can predict ACs efficiently in large networks and real-world topological power grids in manageable computational time. Thus, under our strategy, mitigation in large networks is achieved by reinforcing nodes with large ACs. The framework developed in this study can be implemented in other complex processes that require longer computational time to simulate large networks.
电网中的级联故障由节点和链路组成，具有非局部传播特性。在局部扰动后，相继的结果可能远离源。由于雪崩故障可能意外传播，因此在制定缓解策略时必须谨慎。本文提出了一种缓解这种级联故障的策略。首先，为了描述每个节点对雪崩动力学的影响，我们提出了一种新的度量方法，即雪崩中心度（AC）。然后，基于 AC，可以识别和选择潜在需要加固的节点进行缓解。与启发式措施相比，AC 在减小雪崩规模方面已被证明是有效的；但是，由于非局部传播，计算 AC 可能会耗费大量计算资源。为了解决这个问题，我们使用了图神经网络（GNN）。我们首先使用大量小网络训练 GNN；然后，一旦训练完成，GNN 就可以在大型网络和现实世界的拓扑电网中高效地预测 AC，并在可管理的计算时间内完成。因此，在我们的策略下，通过加强具有大 AC 的节点来实现大型网络的缓解。本研究中开发的框架可以在其他需要更长计算时间来模拟大型网络的复杂过程中实施。
