State estimator for electrical distribution systems based on a particle filter
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Bibliographic record
Abstract
This paper presents a state estimator based on the use of a particle filter (PF). Unlike other types of filters, a PF is suitable for both nonlinear systems and non-Gaussian error distributions. The proliferation of distributed energy resources such as distributed generators and controllable loads has been accompanied by a high degree of uncertainty because the lack of sensors necessitates the use of pseudo-measurements rather than real measurements. For this reason, the proposed state estimator was tested using non-accurate measurements. Bus voltages and angles were chosen as state variables. A comparison of the PF with an extended Kalman filter (EKF) on a 5-node distribution system revealed that the PF provides a very high level of performance, superior to that obtained with the EKF. The proposed estimator was further tested on an IEEE 34-node.
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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