analiza aiqbench
The paper uses a flow-matching generative model as a learned prior for power distribution state estimation, while the power flow physics and measurements are handled separately at sampling time. The reported gains are a 6.7-7.5% CRPS reduction over a Gaussian prior on the IEEE 33-bus feeder and better-calibrated 90% intervals than supervised estimators. For ML readers, the useful point is the design choice: the network models only the unknown injection distribution, so physical consistency comes from the solver rather than from learned approximation, and one trained prior transfers across measurement sets and topologies. The results are benchmark-scale simulations on two feeders, so real-world grid deployment remains untested.
Bayesian Distribution System State Estimation with Flow Matching
arXiv:2610.40728v1 Announce Type: new Abstract: Medium-voltage feeders have few real-time measurements, so distribution system state estimation (DSSE) rests on pseudo-measurements of the secondary substations, a prior in Bayesian terms. Weighted least squares keeps the power flow exact but reduces this prior to a mean and variance, while supervised deep learning estimators also learn the power flow and are tied to one measurement set. We propose PoSE (posterior sampling for state estimation), which keeps the Bayesian structure of DSSE and learns only what the distribution system operator does not know, namely the distribution of substation injections, modelled by a neural network trained with flow matching on historical data. The power flow equations, grid topology and measurements enter only at sampling, through a noise-aware Kalman-type correction. The neural network therefore never approximates the power flow, but every posterior sample is a physically consistent operating point, and one trained prior serves any measurement set, noise level and topology. On the IEEE 33-bus feeder, PoSE lowers the probabilistic injection error (CRPS) by 6.7-7.5% against a full-covariance Gaussian prior in all 12 tested settings. It provides reliable uncertainty: the 90% intervals contain the true injection almost exactly as often as exact posterior samples would, whereas those of supervised estimators contain it in only 37-78% of cases. On the 240-bus unbalanced three-phase Iowa feeder, it gives the lowest injection, voltage and branch-current errors of all estimators. A fast variant matches this accuracy at 0.2-0.6s per snapshot and, without retraining, beats Gaussian priors and retrained supervised estimators on seven reconfigured topologies.
Abstract:Medium-voltage feeders have few real-time measurements, so distribution system state estimation (DSSE) rests on pseudo-measurements of the secondary substations, a prior in Bayesian terms. Weighted least squares keeps the power flow exact but reduces this prior to a mean and variance, while supervised deep learning estimators also learn the power flow and are tied to one measurement set. We propose PoSE (posterior sampling for state estimation), which keeps the Bayesian structure of DSSE and learns only what the distribution system operator does not know, namely the distribution of substation injections, modelled by a neural network trained with flow matching on historical data. The power flow equations, grid topology and measurements enter only at sampling, through a noise-aware Kalman-type correction. The neural network therefore never approximates the power flow, but every posterior sample is a physically consistent operating point, and one trained prior serves any measurement set, noise level and topology. On the IEEE 33-bus feeder, PoSE lowers the probabilistic injection error (CRPS) by 6.7-7.5% against a full-covariance Gaussian prior in all 12 tested settings. It provides reliable uncertainty: the 90% intervals contain the true injection almost exactly as often as exact posterior samples would, whereas those of supervised estimators contain it in only 37-78% of cases. On the 240-bus unbalanced three-phase Iowa feeder, it gives the lowest injection, voltage and branch-current errors of all estimators. A fast variant matches this accuracy at 0.2-0.6s per snapshot and, without retraining, beats Gaussian priors and retrained supervised estimators on seven reconfigured topologies.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
arXiv:2610.40728 [cs.LG]
(or arXiv:2610.40728v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.40728 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 349 cuvinte
Subiecte conexe
Nonparametric autoregressive estimation of probability distributions with parallel gradient boosting
arXiv · 23 oct. 2026
ALICE: In-context, Zero-shot, Mutual Information Estimation
arXiv · 29 sept. 2026
A Mike's-Eye View of ARC's Research
AI Alignment Forum · 9 iun. 2026
Conditional Flow Models for Offline Maximum Entropy Reinforcement Learning
arXiv · 27 oct. 2026