论文标题
使用图神经网络对非局部级联失败的预测和缓解
Prediction and mitigation of nonlocal cascading failures using graph neural networks
论文作者
论文摘要
电力网格中的级联故障（CFS）非局部传播；局部干扰后，第二次故障可能是遥远的。为了研究非本地CFS的雪崩动态和缓解策略，需要数值模拟。但是，计算复杂性很高。在这里，我们首先提出了每个节点的雪崩中心性（AC），这是基于Motter和LAI模型的雪崩大小相关的度量。其次，我们在小型网络中使用AC训练图形神经网络（GNN）。接下来，训练有素的GNN预测在更大的网络和现实世界电网中的AC排名。该结果可以有效地用于缓解雪崩。我们开发的框架可以在其他复杂的过程中实现，这些过程在计算上昂贵，以在大型网络中模拟。
Cascading failures (CFs) in electrical power grids propagate nonlocally; After a local disturbance, the second failure may be distant. To study the avalanche dynamics and mitigation strategy of nonlocal CFs, numerical simulation is necessary; however, computational complexity is high. Here, we first propose an avalanche centrality (AC) of each node, a measure related to avalanche size, based on the Motter and Lai model. Second, we train a graph neural network (GNN) with the AC in small networks. Next, the trained GNN predicts the AC ranking in much larger networks and real-world electrical grids. This result can be used effectively for avalanche mitigation. The framework we develop can be implemented in other complex processes that are computationally costly to simulate in large networks.
