State Estimation as In-context Tabular Prediction

Ayushi Jolotia; Parikshit Pareek

eess.SYOct 23, 2026

State Estimation as In-context Tabular Prediction

Authors: Ayushi Jolotia, Parikshit Pareek

Organizations: Department of Electrical Engineering, Indian Institute of Technology Roorkee.

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 measurement vectors and corresponding state estimates serve as the context, and a new measurement vector forms the query for which we predict a state vector. We propose to use tabular foundation models (TFMs), pretrained on synthetic data, whose weights are never updated, so power system data is needed only as context. With frozen TFM weights we estimate the state of 14-, 30-, 33-, 70-, and 118-bus transmission and distribution networks, and do so where weighted least squares (WLS) fails or degrades, under low observability and with bad data. ICTP errors are 11 to 57 $\times$ below the per-bus average of the context at full metering, and 1.3 to 4.0 $\times$ below it with only 5% of measurements available. With measurements at six buses biased by 8 $σ$ , the ICTP angle error rises 1.8 to 5.0 $\times$ over its bias-free value against 2.5 to 12.7 $\times$ for WLS with bad-data detection. Without retraining, ICTP has lower voltage error than DSS $^2$ on both of its feeders, and lower than a retrained TAPINE on two of three reconfiguration sets.

Figures & tables

Panel of Figure 1 — Fig. 1: Proposed in-context state estimation via TFMs. The query consists of the current meter readings for which the state vector is to be predicted.

Fig. 2: Test A results. Top , A2 angle error. Bottom , A3 voltage error. Crosses mark levels where WLS returned no estimate, the red line is the context mean.

Fig. 3: Left Test B1, voltage error on 118-bus at raised noise, dotted line, WLS (base noise). Center and right , Test C angle error ratio to clean value.

Fig. 4: TU-Delft DSS 2 comparison on their 1080-snapshot test file with their published checkpoint. Voltage RMSE in

10^{-3}

p.u. and line loading in %. WLS’s 70-bus line loading,

1.3\times 10^{4}

%, is off-scale and omitted.

Fig. 5: Feeder reconfiguration under TAPINE’s convention [ 3 ] . WLS values are as cited, tr. refers to TAPINE’s transferred model, and re. retrained.

Robust Estimation

Robust estimation infers reliable parameters and states despite outliers, heavy-tailed noise, data contamination, or model uncertainty.

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Power Systems Tabular Foundation Models

Open paper AskPDF arXiv

Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investigate whether a pretrained tabular foundation model (TFM), TabPFN, can serve as a general-purpose neural solver for power grid steady-state analysis. We evaluate the TFM on three tasks: power flow (PF) prediction, N-k contingency screening (NCS), and voltage stability margin (VSM) prediction. We performed experiments on 118- and 2000-bus systems, comparing the TFM against task-specific neural baselines. Without retraining or fine-tuning, the TFM matches or outperforms the best task-specific model in several settings across the three tasks. For 118-bus PF prediction, a variant of the TFM achieves errors of

1\times 10^{-3}

pu for voltage magnitudes and

0.30^\circ

for voltage angles, outperforming all baselines. On unseen N-1 contingency and close-to-infeasible PF cases, the TFM is competitive with the DCPF approximation. For millions of unseen N-2 contingencies, the TFM achieves AUCs of 0.92 and 0.95 for branch and voltage violation screening, respectively. The TFM also attains accurate VSM prediction performance for the 2000-bus system with limited labelled data. The TFM attains strong results across the tested tasks despite pretraining without any power-system-specific dataset and requiring no fine-tuning step. These results offer a glimpse into the potential of pretrained TFMs as a neural solver for a range of power grid analysis tasks.

Abstract

Robust Estimation

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