Practical Considerations in the Design of Distribution State Estimation Techniques
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Bibliographic record
Abstract
Distribution state estimation is crucial for planning and operation of active distribution networks. This paper extends two state-of-the-art state estimation techniques, namely Weighted Least Squares (WLS) and Ensemble Kalman Filter (EnKF), to unbalanced three-phase distribution networks. These networks are assumed to be equipped with smart meters and distribution- level phasor measurement units (D-PMUs), which are capable of measuring voltage and current phasors. We evaluate the two state estimation methods through extensive simulations in realistic settings where the secondary (low voltage) distribution system is accurately modelled, D-PMUs are installed only at a small number of buses in the primary system, and their measurements are noisy and become available for state estimation after a certain delay. Our results indicate that both methods achieve a sufficiently low error despite the small number of installed D-PMUs, and while EnKF outperforms WLS in some scenarios, the difference between the results gets smaller with more accurate D-PMU measurements. When both voltage and current phasor measurements are available, WLS yields more accurate results under realistic assumptions and is therefore more suitable for real-world applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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