Cover Image

Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pareek, Parikshit, Misra, Sidhant, Deka, Deepjyoti
Format:	Preprint
Published:	2023
Subjects:	Systems and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
by: Pareek, Parikshit, et al.
Published: (2023)

Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach
by: Pareek, Parikshit, et al.
Published: (2024)

Power Flow Approximations for Multiphase Distribution Networks using Gaussian Processes
by: Glover, Daniel, et al.
Published: (2025)

Limitations of Fault-Tolerant Quantum Linear System Solvers for Quantum Power Flow
by: Pareek, Parikshit, et al.
Published: (2024)

PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels
by: Li, Wenting, et al.
Published: (2021)

Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
by: Veedu, Mishfad Shaikh, et al.
Published: (2023)

Gaussian Processes in Power Systems: Techniques, Applications, and Future Works
by: Tan, Bendong, et al.
Published: (2025)

Efficient Policy Adaptation for Voltage Control Under Unknown Topology Changes
by: Feng, Jie, et al.
Published: (2026)

Towards AC Feasibility of DCOPF Dispatch
by: Boateng, Michael A., et al.
Published: (2025)

Mitigating the Impact of Uncertain Wildfire Risk on Power Grids through Topology Control
by: Zhou, Yuqi, et al.
Published: (2023)

Conservative Bias Linear Power Flow Approximations: Application to Unit Commitment
by: Buason, Paprapee, et al.
Published: (2024)

Sample-Based Piecewise Linear Power Flow Approximations Using Second-Order Sensitivities
by: Buason, Paprapee, et al.
Published: (2025)

An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
by: Zhang, Ling, et al.
Published: (2023)

Topology Learning of unknown Networked Linear Dynamical System excited by Cyclostationary inputs
by: Doddi, Harish, et al.
Published: (2020)

Global Performance Guarantees for Neural Network Models of AC Power Flow
by: Chevalier, Samuel, et al.
Published: (2022)

Adaptive Power Flow Approximations with Second-Order Sensitivity Insights
by: Buason, Paprapee, et al.
Published: (2024)

A Data-Driven Sensor Placement Approach for Detecting Voltage Violations in Distribution Systems
by: Buason, Paprapee, et al.
Published: (2022)

Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPs
by: Omidi, Saber, et al.
Published: (2025)

Watts vs. Bytes: Turning Data Centers into Grid Assets via Storage Compute Co-Optimization
by: Liu, Shaohui, et al.
Published: (2026)

Sparse Neural Approximations for Bilevel Adversarial Problems in Power Grids
by: Cho, Young-ho, et al.
Published: (2025)

Scaling Laws of Machine Learning for Optimal Power Flow
by: Liu, Xinyi, et al.
Published: (2026)

Learning the Optimal Power Flow: Environment Design Matters
by: Wolgast, Thomas, et al.
Published: (2024)

Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
by: Pineda, Salvador, et al.
Published: (2024)

Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
by: Li, Zeyang, et al.
Published: (2026)

A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
by: Xia, Weijie, et al.
Published: (2024)

Data-driven Reachability Verification with Probabilistic Guarantees under Koopman Spectral Uncertainty
by: Ding, Jianqiang, et al.
Published: (2025)

Hierarchical Upper Confidence Bounds for Constrained Online Learning
by: Baheri, Ali
Published: (2024)

A Generalizable Physics-informed Learning Framework for Risk Probability Estimation
by: Wang, Zhuoyuan, et al.
Published: (2023)

Marginalize, Rather than Impute: Probabilistic Wind Power Forecasting with Incomplete Data
by: Wen, Honglin, et al.
Published: (2024)

Residual Power Flow for Neural Solvers
by: Stiasny, Jochen, et al.
Published: (2026)

Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach
by: Pan, Huazi, et al.
Published: (2025)

Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning
by: De Lellis, Francesco, et al.
Published: (2023)

Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification
by: Alsmeier, Hendrik, et al.
Published: (2025)

Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
by: Bose, Shourya, et al.
Published: (2026)

The value of storage in electricity distribution: The role of markets
by: Lauinger, Dirk, et al.
Published: (2025)

Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
by: Chen, Kaixuan, et al.
Published: (2024)

MoE-GraphSAGE-Based Integrated Evaluation of Transient Rotor Angle and Voltage Stability in Power Systems
by: Zhang, Kunyu, et al.
Published: (2025)

Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context
by: Xu, Jianyu, et al.
Published: (2024)

ECO: Energy-Constrained Operator Learning for Chaotic Dynamics with Boundedness Guarantees
by: Goertzen, Andrea, et al.
Published: (2025)

A Hybrid Strategy for Probabilistic Forecasting and Trading of Aggregated Wind-Solar Power: Design and Analysis in HEFTCom2024
by: Pu, Chuanqing, et al.
Published: (2025)