The paper introduces PoSE, a Bayesian posterior sampling method for distribution system state estimation that learns the distribution of substation injections via a neural network trained with flow matching. Unlike weighted least squares or supervised deep learning, PoSE preserves the full Bayesian structure and ensures every posterior sample satisfies the power flow equations. Experiments on IEEE 33‑bus and Iowa 240‑bus feeders show PoSE reduces injection error by up to 7.5% and provides more reliable uncertainty estimates than Gaussian priors or supervised estimators.
arXiv:2610.32487v1 Announce Type: cross Abstract: This letter poses state estimation (SE) as an in-context tabular prediction (ICTP) problem. The key idea is a tabular arrangement in which historical...
arXiv:2604.22784v2 Announce Type: replace Abstract: Power System State Estimation (PSSE) converts geographically distributed measurements into the voltage magnitudes and phase angles needed for grid...
arXiv:2308. 07867v4 Announce Type: replace-cross Abstract: The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy.
arXiv:2607. 03217v1 Announce Type: new Abstract: Uncertainty quantification of hydrological predictions is necessary to inform operational decisions.
The paper introduces the Belief Flow Filter (BFF), a generative filtering framework that encodes the evolving posterior distribution directly into flow matching model weights and updates them via test‑time gradient descent. By avoiding particle representations and Gaussian assumptions, BFF aligns structurally with Bayesian filtering and targets the recursive filtering operator. Empirical results on five physical systems—including chaotic dynamics, sparse observations, and a tokamak plasma estimation task—show that BFF outperforms existing methods in most benchmark metrics.
The paper introduces learned control variates (CVs) to estimate rare grid‑outage probabilities at electric vehicle charging stations more efficiently. By training a classifier on inexpensive features such as temperature and load forecasts, the CV reduces variance in Monte Carlo simulations, with performance tied to the classifier’s recall and false‑positive rate. Experiments on a feeder‑overload simulator and a real Surabaya network show that even a weak learner can cut variance by a quarter, while a boosted‑tree learner achieves about 60% reduction, leading to a one‑third decrease in city‑wide blackout days compared to standard Monte Carlo.
arXiv:2608. 09921v1 Announce Type: new Abstract: Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced.
arXiv:2510. 22048v4 Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces).
The paper introduces a method that uses a single deep neural network, trained only on basecase AC power flow data, to predict post-contingency operating states for any single-line outage. It frames this prediction as a fixed-point iteration and provides sufficient convergence conditions, certifying them via semidefinite programming. Numerical experiments on the IEEE 118-bus system show that the certified conditions hold for all tested contingencies and that accurate state estimates are achieved in only a few iterations.
The paper presents a unified benchmark of graph neural network (GNN) architectures for predicting AC power flow (ACPF) on IEEE 14‑bus and 118‑bus systems. It isolates the effects of message‑passing mechanisms and inductive biases by using identical preprocessing, training procedures, and comparable parameter budgets. The study finds that anisotropic message‑passing, explicit edge features for line impedances, and global context aggregation yield the best trade‑off between predictive accuracy and physical consistency, while two or three message‑passing layers are generally optimal under a fixed parameter budget.
arXiv:2605. 29072v2 Announce Type: replace Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management.
arXiv:2605.23194v2 Announce Type: replace-cross Abstract: Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are ofte...