Guardado en:
| Autores principales: | Zhang, Ling, Tabas, Daniel, Zhang, Baosen |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2304.01409 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
por: Stiasny, Jochen, et al.
Publicado: (2023)
por: Stiasny, Jochen, et al.
Publicado: (2023)
Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions
por: Za'ter, Muhy Eddin, et al.
Publicado: (2025)
por: Za'ter, Muhy Eddin, et al.
Publicado: (2025)
Residual Power Flow for Neural Solvers
por: Stiasny, Jochen, et al.
Publicado: (2026)
por: Stiasny, Jochen, et al.
Publicado: (2026)
Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
por: Rosemberg, Andrew, et al.
Publicado: (2025)
por: Rosemberg, Andrew, et al.
Publicado: (2025)
DiffOPF: Diffusion Solver for Optimal Power Flow
por: Hoseinpour, Milad, et al.
Publicado: (2025)
por: Hoseinpour, Milad, et al.
Publicado: (2025)
Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
por: Pareek, Parikshit, et al.
Publicado: (2023)
por: Pareek, Parikshit, et al.
Publicado: (2023)
Learning to Pursue AC Optimal Power Flow Solutions with Feasibility Guarantees
por: Ajeyemi, Damola, et al.
Publicado: (2025)
por: Ajeyemi, Damola, et al.
Publicado: (2025)
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
por: Lefringhausen, Robert, et al.
Publicado: (2023)
por: Lefringhausen, Robert, et al.
Publicado: (2023)
Scaling Laws of Machine Learning for Optimal Power Flow
por: Liu, Xinyi, et al.
Publicado: (2026)
por: Liu, Xinyi, et al.
Publicado: (2026)
Learning the Optimal Power Flow: Environment Design Matters
por: Wolgast, Thomas, et al.
Publicado: (2024)
por: Wolgast, Thomas, et al.
Publicado: (2024)
Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks
por: Christianson, Nicolas, et al.
Publicado: (2024)
por: Christianson, Nicolas, et al.
Publicado: (2024)
Global Performance Guarantees for Neural Network Models of AC Power Flow
por: Chevalier, Samuel, et al.
Publicado: (2022)
por: Chevalier, Samuel, et al.
Publicado: (2022)
Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
por: Pineda, Salvador, et al.
Publicado: (2024)
por: Pineda, Salvador, et al.
Publicado: (2024)
Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics
por: Zheng, Jialin, et al.
Publicado: (2025)
por: Zheng, Jialin, et al.
Publicado: (2025)
Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
por: de Jong, Thomas, et al.
Publicado: (2024)
por: de Jong, Thomas, et al.
Publicado: (2024)
PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
por: Klamkin, Michael, et al.
Publicado: (2025)
por: Klamkin, Michael, et al.
Publicado: (2025)
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees
por: Zhang, Ryan, et al.
Publicado: (2024)
por: Zhang, Ryan, et al.
Publicado: (2024)
Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees
por: Stamouli, Charis, et al.
Publicado: (2024)
por: Stamouli, Charis, et al.
Publicado: (2024)
Optimal Voltage Control Using Online Exponential Barrier Method
por: Zhang, Peng, et al.
Publicado: (2025)
por: Zhang, Peng, et al.
Publicado: (2025)
Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control
por: Tabas, Sadegh Sadeghi, et al.
Publicado: (2024)
por: Tabas, Sadegh Sadeghi, et al.
Publicado: (2024)
Reduced Optimal Power Flow Using Graph Neural Network
por: Pham, Thuan, et al.
Publicado: (2022)
por: Pham, Thuan, et al.
Publicado: (2022)
Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow
por: Pham, Thuan, et al.
Publicado: (2024)
por: Pham, Thuan, et al.
Publicado: (2024)
Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach
por: Pan, Huazi, et al.
Publicado: (2025)
por: Pan, Huazi, et al.
Publicado: (2025)
Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansion
por: Li, Haoyu, et al.
Publicado: (2025)
por: Li, Haoyu, et al.
Publicado: (2025)
A General Approach of Automated Environment Design for Learning the Optimal Power Flow
por: Wolgast, Thomas, et al.
Publicado: (2025)
por: Wolgast, Thomas, et al.
Publicado: (2025)
Feasible Policy Iteration for Safe Reinforcement Learning
por: Yang, Yujie, et al.
Publicado: (2023)
por: Yang, Yujie, et al.
Publicado: (2023)
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
por: Puech, Alban, et al.
Publicado: (2025)
por: Puech, Alban, et al.
Publicado: (2025)
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
por: Castañeda, Fernando, et al.
Publicado: (2022)
por: Castañeda, Fernando, et al.
Publicado: (2022)
N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network
por: Pham, Thuan, et al.
Publicado: (2024)
por: Pham, Thuan, et al.
Publicado: (2024)
Building Power Grid Models from Open Data: A Complete Pipeline from OpenStreetMap to Optimal Power Flow
por: Britto, Andrea, et al.
Publicado: (2026)
por: Britto, Andrea, et al.
Publicado: (2026)
Two-Stage Stochastic Optimal Power Flow for Microgrids With Uncertain Wildfire Effects
por: Chowdhury, Sifat, et al.
Publicado: (2024)
por: Chowdhury, Sifat, et al.
Publicado: (2024)
Pick-to-Learn for Systems and Control: Data-driven Synthesis with State-of-the-art Safety Guarantees
por: Paccagnan, Dario, et al.
Publicado: (2025)
por: Paccagnan, Dario, et al.
Publicado: (2025)
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
por: Zhang, Thomas T., et al.
Publicado: (2024)
por: Zhang, Thomas T., et al.
Publicado: (2024)
Geometry of the Feasible Output Regions of Grid-Interfacing Inverters with Current Limits
por: Streitmatter, Lauren, et al.
Publicado: (2025)
por: Streitmatter, Lauren, et al.
Publicado: (2025)
Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
por: Glover, Daniel, et al.
Publicado: (2025)
por: Glover, Daniel, et al.
Publicado: (2025)
Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
por: Pham, Thuan, et al.
Publicado: (2024)
por: Pham, Thuan, et al.
Publicado: (2024)
Discrete Shortest Paths in Optimal Power Flow Feasible Regions
por: Turizo, Daniel, et al.
Publicado: (2024)
por: Turizo, Daniel, et al.
Publicado: (2024)
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
por: Chen, Kaixuan, et al.
Publicado: (2024)
por: Chen, Kaixuan, et al.
Publicado: (2024)
Optimal Control of Grid-Interfacing Inverters With Current Magnitude Limits
por: Joswig-Jones, Trager, et al.
Publicado: (2023)
por: Joswig-Jones, Trager, et al.
Publicado: (2023)
Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
por: Li, Haoyu, et al.
Publicado: (2025)
por: Li, Haoyu, et al.
Publicado: (2025)
Ejemplares similares
-
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
por: Stiasny, Jochen, et al.
Publicado: (2023) -
Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions
por: Za'ter, Muhy Eddin, et al.
Publicado: (2025) -
Residual Power Flow for Neural Solvers
por: Stiasny, Jochen, et al.
Publicado: (2026) -
Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
por: Rosemberg, Andrew, et al.
Publicado: (2025) -
DiffOPF: Diffusion Solver for Optimal Power Flow
por: Hoseinpour, Milad, et al.
Publicado: (2025)