使用超参数扩散模型预测连锁故障
Predicting Cascading Failures with a Hyperparametric Diffusion Model
AI总结：
提出融合物理概念与超参数IC模型的马尔可夫局部随机扩散模型，从连锁故障轨迹中学习传播权重，以预测未见电网配置中的故障扩散并指导加固决策。
摘要：
本文中，我们通过信息扩散模型的视角研究电网中的连锁故障。与在线社交网络中谣言或影响力的传播类似，人们已经观察到，电网中的故障（停电）会在病毒式传播机制的驱动下发生传染性扩散。我们采用一种随机扩散模型，该模型具有马尔可夫性（无记忆）和局部性（一个节点即一条输电线路的激活只能由其邻居引起）。我们的模型将病毒式扩散原理与基于物理的概念相结合，把扩散权重（输电线路之间的传染概率）与超参数 Information Cascades（IC，信息级联）模型相关联。我们表明，该扩散模型可以从连锁故障轨迹中学习，从而实现对故障传播的准确建模与预测。该方法通过成熟且高效的图分析方法和图扩散仿真，提供可付诸行动的信息。此外，通过利用超参数模型，即使在未见过的电网配置中，我们也能预测扩散并降低连锁故障风险，而现有方法会因缺乏训练数据而表现不佳。基于一个基准电网及其内部仿真开展的大量实验表明，我们的方法有效捕捉了故障扩散现象，并指导加固电网的决策，降低了大规模连锁故障的风险。此外，我们还刻画了模型的样本复杂度，改进了现有界。
英文摘要：
In this paper, we study cascading failures in power grids through the lens of information diffusion models. Similar to the spread of rumors or influence in an online social network, it has been observed that failures (outages) in a power grid can spread contagiously, driven by viral spread mechanisms. We employ a stochastic diffusion model that is Markovian (memoryless) and local (the activation of one node, i.e., transmission line, can only be caused by its neighbors). Our model integrates viral diffusion principles with physics-based concepts, by correlating the diffusion weights (contagion probabilities between transmission lines) with the hyperparametric Information Cascades (IC) model. We show that this diffusion model can be learned from traces of cascading failures, enabling accurate modeling and prediction of failure propagation. This approach facilitates actionable information through well-understood and efficient graph analysis methods and graph diffusion simulations. Furthermore, by leveraging the hyperparametric model, we can predict diffusion and mitigate the risks of cascading failures even in unseen grid configurations, whereas existing methods falter due to a lack of training data. Extensive experiments based on a benchmark power grid and simulations therein show that our approach effectively captures the failure diffusion phenomena and guides decisions to strengthen the grid, reducing the risk of large-scale cascading failures. Additionally, we characterize our model's sample complexity, improving upon the existing bound.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
