Power Systems

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Period ending 2026-09-21

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Power Systems

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Ac Optimal Power Flow

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Smart Grids

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Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
May 7, 2026Tianxin Zhou, Xiang Li, Haibing LuPower Systems Heterogeneous Graph Transformers
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
May 7, 2026Dhruv Suri, Helgi Hilmarsson, Shourya BoseAc Optimal Power Flow Cold-Start
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
May 6, 2026Jae-Won Chung, Zhirui Liang, Yanyong Mao +3Artificial Intelligence Data Centers Smart Grids
Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
May 5, 2026Job Groeneveld, Miguel Muñoz, Jan Viebahn +1Power Systems Multi-Objective Optimization
MARS-DA: A Hierarchical Reinforcement Learning Framework for Risk-Aware Multi-Agent Bidding in Power Grids
May 4, 2026Jiayi Chen, Xuan Zhang, Guiling WangElectricity Markets Power Systems
From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design
May 4, 2026Noman Bashir, Rob Sherwood, Le Xie +1Artificial Intelligence Data Centers Data Centers
LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
May 4, 2026Hongwei Jin, Keunju Song, Zeeshan Memon +5Ac Optimal Power Flow Accurate Surrogate Models
Towards Systematic Generalization for Power Grid Optimization Problems
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Apr 27, 2026Yihong Zhou, Hongtai Zeng, Thomas MorstynSmart Grids Power Systems
An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids
Apr 26, 2026Ahmad Mohammad Saber, Ahmed Saber Refae, Davor Svetinovic +4Smart Grids Voltage
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Apr 25, 2026Taha Hammadia, Lucas Rea, Ahmad Mohammad Saber +2Large Language Model Jailbreaks Jailbreaks
A Network-Aware Evaluation of Distributed Energy Resource Control in Smart Distribution Systems
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Predicting Power-System Dynamic Trajectories with Foundation Models
Apr 16, 2026Haoran Li, Lihao Mai, Chenhan Xiao +2Power Systems Trajectory Prediction
Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks
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AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
Mar 27, 2026Borui Zhang, Nariman Mahdavi, Subbu Sethuvenkatraman +1Power Systems
Generating adversarial inputs for a graph neural network model of AC power flow
Feb 20, 2026Robert ParkerAc Optimal Power Flow Neural Network Verification
Solving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the AC Optimal Power Flow
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Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
May 23, 2025Julian Oelhaf, Georg Kordowich, Andreas Maier +2Voltage Power Systems

May 7, 2026 · cs.LGJ/K move · Enter open · S save

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

Tianxin Zhou, Xiang Li, Haibing Lu

Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure correlations from cascade data, but they are trained and evaluated on a single grid, and transferring the learned knowledge to an unseen grid remains an open problem. We address this by training a single Gated Recurrent Unit (GRU)-gated Graph Attention Network on combined cascading failure data from limited training grids and applying it directly to any unseen grid without retraining. A GRU gate controls what information each node retains or discards at each cascade iteration. Empirical evaluation shows that the model transfers zero-shot to multiple new grids spanning inter-time and inter-domain settings. Using information extracted from the trained model, we consistently identify more vulnerable lines than established structural and electrical baselines.

Power Systems Heterogeneous Graph Transformers Gated Recurrent Unit

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