Online Tracking of Voltage-Dependent Load Parameters Using ULTC Created Disturbances
Bibliographic record
Abstract
This paper presents a field-test verified method for continuous tracking of voltage-dependent load model parameters. The basic idea is to use the natural or automatic movement of the under load tap changer (ULTC) as the source of voltage disturbance. Load parameters are estimated from each taping activity. While the idea of using manual ULTC tapping disturbances for load parameter estimation is not new, this paper has demonstrated, through laboratory tests and field measurements, that load parameters can be estimated from the automatic ULTC movements. Since a ULTC often has many movements in any given day, the load parameters can be collected in a consistent, frequent, and predictable manner and, consequently, online estimation and tracking of load parameters becomes feasible. The paper presents the implementation of the proposed method and its field measurement experiences, especially the characteristics of tap movements and the associated load responses. Experiments and a large number of field measurements presented in this paper have shown that the proposed method is a practical and effective method for determining load parameters online.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".