Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

Power Grid Cascading Failure Mitigation by Reinforcement Learning

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL). The motivation of the Multi-Stage Cascading Failure (MSCF) problem and its connection with the challenge of climate change are introduced. The bottom-level corrective control of the MCSF problem is formulated based on DCOPF (Direct Current Optimal Power Flow). Then, to mitigate the MSCF issue by a high-level RL-based strategy, physics-informed reward, action, and state are devised. Besides, both shallow and deep neural network architectures are tested. Experiments on the IEEE 118-bus system by the…

强化学习用于缓解级联故障：通过敏感度因子的靶向探索

RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors

本文针对电网弹性与气候变化的相互影响问题，提出物理引导强化学习（PG-RL）框架，通过利用潮流敏感度因子引导探索，确定实时补救线路切换动作，提升电网资源利用率并优化停电缓解策略，增强电网弹性。

基于因果推断的连锁故障预测

Cascading Failure Prediction via Causal Inference

本文提出面向输电网络的因果推断框架，构建有向潜在图刻画线路间局部与非局部因果依赖，并设计两种算法识别高概率、高代价连锁故障场景。

基于GRU门控图注意力的归纳性电力网络级联故障分析

Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention

本文提出一种基于GRU门控图注意力网络的方法，能有效识别电力网络中潜在的脆弱传输线路，适用于多电网跨时间和领域场景。

使用超参数扩散模型预测连锁故障

Predicting Cascading Failures with a Hyperparametric Diffusion Model

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。

Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks

We use machine learning tools to model the line interaction of failure cascading in power grid networks. We first collect data sets of simulated trajectories of possible consecutive line failure following an initial random failure and considering actual constraints in a model power network until the system settles at a steady state. We use weighted $l_1$-regularized logistic regression-based models to find static and dynamic models that capture pairwise and latent higher-order lines' failure interactions using pairwise statistical data. The static model captures the failures' interactions near…