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Bayesian Distribution System State Estimation with Flow Matching — arXiv:2610.40728

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40728
License (as archived): CC0-1.0
Archived: 2026-11-02T07:09:47.215Z
SHA-256 of the archived text: ea7eb306d17f58a04c37d706870f09e59aa787d57df2d817c99cf4a1d5cd4831
Stable URL of this page: https://metraipolis.com/documento/dc_2ea0501feb53

How to cite

Bayesian Distribution System State Estimation with Flow Matching — arXiv:2610.40728 — arXiv (en), https://arxiv.org/abs/2610.40728 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ea0501feb53 (Metraipolis archive dc_2ea0501feb53, sha256 ea7eb306d17f58a04c37d706870f09e59aa787d57df2d817c99cf4a1d5cd4831)

BibTeX

@misc{dc2ea0501feb53,
  title = {Bayesian Distribution System State Estimation with Flow Matching — arXiv:2610.40728},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40728},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ea0501feb53; sha256 ea7eb306d17f58a04c37d706870f09e59aa787d57df2d817c99cf4a1d5cd4831},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40728. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2610.40728 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:06:59.395Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).