arXiv AI July 21, 2026 By Jiagang Qu, Yong Tao, Dan Wang, Enyi Li, Jingjing Qi, Ding Wang

Neural Controlled Differential Equations for EMT-Level Surrogate Modeling of Grid-Forming Inverters

arXiv:2607. 16258v1 Announce Type: cross Abstract: The application of artificial intelligence methods in power electronic converter modeling is becoming increasingly widespread, but existing applications still face many challenges, such as difficulties in multi-time-scale hybrid analysis and the lack of physics-aware evaluation criteria and constraints, resulting in poor performance.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 11

Tools to Explain Neural Networks for Power System Dynamics

arXiv:2608. 08048v1 Announce Type: cross Abstract: 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.

By Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis

arXiv Machine Learning

Aug 5

Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution

arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.

By Chenhan Xiao, Xinyu He, Haoran Li, Hanghang Tong, Yang Weng

diffusion benchmarks

arXiv Machine Learning

1d ago

A Unified Mamba--MoE Surrogate for Closed-Loop Simulation and Measurement-Window Forecasting of Inverter Transients

arXiv:2608. 15051v1 Announce Type: new Abstract: This paper proposes a Mamba surrogate model with mixture-of-experts (MoE) routing to represent the transient dynamics of inverter-based resources.

By Haoguang Wang, Huy Hoang Le, Akhila Kandivalasa, Christian Moya, Marcos Netto, Guang Lin

arXiv AI

Jun 12

On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators

arXiv:2301. 12538v2 Announce Type: replace-cross Abstract: This paper develops an Operator Learning framework for approximating the dynamic response of synchronous generators.

By Christian Moya, Amirhossein Mollaali, Guang Lin, Meng Yue

fine-tuning

arXiv Machine Learning

Jul 31

Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries

arXiv:2607. 27681v1 Announce Type: new Abstract: Transient-stability assessment determines whether a power system can recover after a disturbance and is therefore essential to preventing generator trips and cascading outages.

By Baoli Hao, Chenxi Hu, Ming Zhong, Ren Wang

arXiv AI

Jul 17

Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network

arXiv:2607. 14321v1 Announce Type: cross Abstract: Incorporating hysteresis and eddy currents into finite element simulations of laminated-core electrical machines is computationally challenging.

By Florent Purnode, Louis Denis, Fran\c{c}ois Henrotte, Gilles Louppe, Christophe Geuzaine

Related stories

Tools to Explain Neural Networks for Power System Dynamics

Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution

A Unified Mamba--MoE Surrogate for Closed-Loop Simulation and Measurement-Window Forecasting of Inverter Transients

On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators

Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries

Accounting for Hysteresis and Eddy Currents in Finite Element Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network