A Complex Domain Gaussian Belief Propagation Method for Fully Distributed State Estimation
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Bibliographic record
Abstract
To alleviate the communication, storage, and computation burden on the control center and make full use of edge computing resources, fully distributed state estimation has received increasing interest recently. This paper intends to improve the efficiency and robustness of the fully distributed state estimation by introducing a meter-level method based on the Gaussian belief propagation theory. Specifically, we propose a complex domain factor graph, which extends the state variable vector from voltage phasors to multiple electrical quantities, including voltage phasors, current phasors, voltage magnitudes, and active/reactive power, enabling the direct processing of nonlinear measurement models and significantly reducing the number of iterations. Furthermore, based on the M-estimation theory, we innovatively incorporate multiple robust functions to the Gaussian belief propagation method to enhance the robustness of the proposed fully distributed estimator. The effectiveness of the proposed method is demonstrated under various operation conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it