Power System Optimization and Stability

Neueste Paper

• RAPTOR: RAndom-projection Physics-informed Transient sOlveR

  Petros Ellinas, Benjamin Vilmann, Spyros Chatzivasileiadis, Johanna Vorwerk · 25. September 2026

  The complexity of time-domain simulation of modern power systems has increased significantly because converter-based resources introduce control dynamics that must be simulated alongside slower system-level and fast electromagnetic dynamics. The resulting wide range of timescales may force classical…

• LLaTSA: Large Language Model-Aligned General-Purpose Transient Stability Analysis

  Chao Shen, Hongwei Zhen, Junyan Shao, Zhenghao Yang, Yifan Zhang, Mingyang Sun · 15. September 2026

  Dynamic trajectory prediction has become an important paradigm for data-driven transient stability analysis (TSA), yet most existing predictors remain system-specific and require substantial retraining when network configurations, generation mixes, or state-variable sets change. Uni-TSA introduced a…

• Physics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification

  Adnan Anwar · 7. September 2026

  Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure classification. Graph neural networks (GNNs) achieve strong predictive performance on this task, but typically require end-to-end training and model-specific tuning, while their latent represent…

• Tools to Explain Neural Networks for Power System Dynamics

  Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis · 11. August 2026

  This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for power system dynamics. Power system simulations are increasingly challenged by stiff and multi-timescale dynamics arisin…

• Diffusion Model-based Parameter Estimation in Dynamic Power Systems

  Feiqin Zhu, Dmitrii Torbunov, Zhongjing Jiang, Tianqiao Zhao, Amirthagunaraj Yogarathnam, Yihui Ren, Meng Yue · 29. Juli 2026

  Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness presents a critical barrier to accurate and unique identification. Here we introduce a parameter estimation framework to address…

• Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference

  Haoran Li, Lihao Mai, Muhao Guo, Jiaqi Wu, Yang Weng · 24. Juli 2026

  Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reliable connectivity records remains challenging in practice due to heterogeneous and imperfect utility data. Existing topology identification methods …

• Revisiting data-driven dynamic security assessment with a tabular foundation model

  Olayiwola Arowolo, Maosheng Yang, Jochen Cremer · 20. Juli 2026

  Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning. Existing approaches face two limitations. First, they require a large labelled database for training, with a separate model trained, tuned, a…

• Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

  Josef Hoppe, Sarra Bouchkati, Farah Nasr, Jonathan Krapp, Alexander Och, Maximilian Wirth, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Michael T. Schaub · 20. Juli 2026

  Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior e…

• Audited Selective Verification for Risk-Controlled N-1 Thermal Contingency Screening under Deployment Shift

  Jayakumar Manoharan · 16. Juli 2026

  Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow is too slow, while fast linear-sensitivity screening gives no statistical guarantee and can silently pass unsafe operating points, especially when a …

• Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids

  Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat, Eman Hammad · 8. Juli 2026

  Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading failures. This paper proposes FedPPO-PG, a Federated Multi-Agent Proximal Policy Optimization framework with Physics-Grounded neighborhoods, which refor…

• Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

  Lihui Liu, Mucun Sun, Caisheng Wang · 9. Juni 2026

  Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions. During an outage, several actions can be taken, such as network reconfiguration through switching operations and emergency load shedding. However, traditional machine learning method…

• Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies

  Dhruvi Khandelwal, Anurag Basistha, Ayushi Jolotia, Parikshit Pareek · 3. Juni 2026

  Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought experiment to answer a fundamental inquiry: how wide must a neural network be to almost accurately approximate the ACOPF man…

• Interpretable Policy Distillation for Power Grid Topology Control

  Aleksandra Dmitruka, Karlis Freivalds · 2. Juni 2026

  Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators. We ask whether a Proximal Policy Optimization (PPO) agent for grid topology control can be c…

• Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models

  Shourya Bose, Helgi Hilmarsson, Dhruv Suri · 13. Mai 2026

  Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on heavily loaded instances near voltage collapse. We prove a lower bound on the Newton-Raphson iteration count that depends…

• Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models

  Shourya Bose, Helgi Hilmarsson, Dhruv Suri · 13. Mai 2026

  Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised approaches generalize poorly on heavily loaded instances near voltage collapse. We prove a lower bound on the Newton-Raphson iteration count that depends…

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

  Tianxin Zhou, Xiang Li, Haibing Lu · 11. Mai 2026

  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 remai…

• Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks

  Solon Falas, Markos Asprou, Charalambos Konstantinou, Maria K. Michael · 28. April 2026

  State estimation is a cornerstone of power system control-center operations, and its robust operation is increasingly a cyber-physical security concern as modern grids become more digitalized and communication-intensive. Neural network-based approaches have gained attention as alternatives to conven…

• Predicting Power-System Dynamic Trajectories with Foundation Models

  Haoran Li, Lihao Mai, Chenhan Xiao, Erik Blasch, Yang Weng · 17. April 2026

  As power systems transition toward renewable-rich and inverter-dominated operations, accurate time-domain dynamic analysis becomes increasingly critical. Such analysis supports key operational tasks, including transient stability assessment, dynamic security analysis, contingency screening, and post…

• Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid Operation

  Gitesh Malik · 16. April 2026

  Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion management. However, its deployment in real-world power systems remains limited by strict safety requirements, brittleness under rare disturbances, and poor generalization to un…

• Unsupervised Detection of Spatiotemporal Anomalies in PMU Data Using Transformer-Based BiGAN

  Muhammad Imran Hossain, Jignesh Solanki, Sarika Khushlani Solanki · 14. April 2026

  Ensuring power grid resilience requires the timely and unsupervised detection of anomalies in synchrophasor data streams. We introduce T-BiGAN, a novel framework that integrates window-attention Transformers within a bidirectional Generative Adversarial Network (BiGAN) to address this challenge. Its…

• Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

  Fausto Mauricio Lagos Suarez, Akshit Saradagi, Vidya Sumathy, Shruti Kotpaliwar, George Nikolakopoulos · 14. April 2026

  This article introduces a novel sample-efficient curriculum learning (CL) approach for training an end-to-end reinforcement learning (RL) policy for robust stabilization of a Quadrotor. The learning objective is to simultaneously stabilize position and yaw-orientation from random initial conditions …

• PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow with Continual Learning

  Chidozie Ezeakunne, Jose E. Tabarez, Reeju Pokharel, Anup Pandey · 19. März 2026

  Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton-Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different …

• Dynamic Load Model for Data Centers with Pattern-Consistent Calibration

  Siyu Lu, Chenhan Xiao, Yang Weng · 10. Februar 2026

  The rapid growth of data centers has made large electronic load (LEL) modeling increasingly important for power system analysis. Such loads are characterized by fast workload-driven variability and protection-driven disconnection and reconnection behavior that are not captured by conventional load m…

• A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction

  Bo Li, Zijun Chen, Haiwang Zhong, Di Cao, Guangchun Ruan · 1. Januar 2026

  In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. Existing data-driven methods are limited by inadaptability to concept drift in power systems, poor robustness under high …

• Scalable Cloud-Native Architectures for Intelligent PMU Data Processing

  Nachiappan Chockalingam, Akshay Deshpande, Lokesh Butra, Ram Sekhar Bodala, Nitin Saksena, Adithya Parthasarathy, Balakrishna Pothineni, Akash Kumar Agarwal · 30. Dezember 2025

  Phasor Measurement Units (PMUs) generate high-frequency, time-synchronized data essential for real-time power grid monitoring, yet the growing scale of PMU deployments creates significant challenges in latency, scalability, and reliability. Conventional centralized processing architectures are incre…