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.

通过信息传播模型，研究电网中的级联故障；通过将传染概率与信息串联模型相关联，建立了稳健的故障扩散模型，有效地预测级联故障传播，减少大规模级联故障的风险。

使用超参数扩散模型预测级联故障

Predicting Cascading Failures with a Hyperparametric Diffusion Model

Higher variability in grid conditions, resulting from growing renewable
penetration and increased incidence of extreme weather events, has increased
the difficulty of screening for scenarios that may lead to catastrophic
cascading failures. Traditional power-flow-based tools for assessing cascading
blackout risk are too slow to properly explore the space of possible failures
and load/generation patterns. We add to the growing literature of faster
graph-neural-network (GNN)-based techniques, developing two novel techniques
for the estimation of blackout magnitude from initial grid conditions. First we
propose several methods for employing an initial classification step to filter
out safe "non blackout" scenarios prior to magnitude estimation. Second, using
insights from the statistical properties of cascading blackouts, we propose a
method for facilitating non-local message passing in our GNN models. We
validate these two approaches on a large simulated dataset, and show the
potential of both to increase blackout size estimation performance.

通过图神经网络技术加快网络条件的初步分类和感知的方法，提高了黑暗规模估计性能，为筛选可能导致灾难性级联故障的情况提供了一种新的工具。

统计增强的图神经网络预测级联断电严重程度

Cascading Blackout Severity Prediction with Statistically-Augmented  Graph Neural Networks

Public Graph Neural Networks (GNN) benchmark datasets facilitate the use of
GNN and enhance GNN applicability to diverse disciplines. The community
currently lacks public datasets of electrical power grids for GNN applications.
Indeed, GNNs can potentially capture complex power grid phenomena over
alternative machine learning techniques. Power grids are complex engineered
networks that are naturally amenable to graph representations. Therefore, GNN
have the potential for capturing the behavior of power grids over alternative
machine learning techniques. To this aim, we develop a graph dataset for
cascading failure events, which are the major cause of blackouts in electric
power grids. Historical blackout datasets are scarce and incomplete. The
assessment of vulnerability and the identification of critical components are
usually conducted via computationally expensive offline simulations of
cascading failures. Instead, we propose using machine learning models for the
online detection of cascading failures leveraging the knowledge of the system
state at the onset of the cascade. We develop PowerGraph, a graph dataset
modeling cascading failures in power grids, designed for two purposes, namely,
i) training GNN models for different graph-level tasks including multi-class
classification, binary classification, and regression, and ii) explaining GNN
models. The dataset generated via a physics-based cascading failure model
ensures the generality of the operating and environmental conditions by
spanning diverse failure scenarios. In addition, we foster the use of the
dataset to benchmark GNN explainability methods by assigning ground-truth
edge-level explanations. PowerGraph helps the development of better GNN models
for graph-level tasks and explainability, critical in many domains ranging from
chemistry to biology, where the systems and processes can be described as
graphs.

开发了一个用于电力网络级联故障的图形数据集，用于训练图神经网络模型以及解释性分析，旨在应用于各个领域，如化学和生物学。

PowerGraph：用于图神经网络的电网基准数据集

PowerGraph: A power grid benchmark dataset for graph neural networks

The control of complex networks is of paramount importance in areas as
diverse as ecosystem management, emergency response, and cell reprogramming. A
fundamental property of networks is that perturbations to one node can affect
other nodes, potentially causing the entire system to change behavior or fail.
Here, we show that it is possible to exploit the same principle to control
network behavior. Our approach accounts for the nonlinear dynamics inherent to
real systems, and allows bringing the system to a desired target state even
when this state is not directly accessible due to constraints that limit the
allowed interventions. Applications show that this framework permits
reprogramming a network to a desired task as well as rescuing networks from the
brink of failure---which we illustrate through the mitigation of cascading
failures in a power-grid network and the identification of potential drug
targets in a signaling network of human cancer.

本文提出了一种可控制复杂网络行为的方法，该方法考虑到真实系统的非线性动力学特性，允许将系统带入所需的目标状态，从而重新编程网络并挽救网络遭受连锁失败的危险。