Improved branch parameter errors detection, identification and correction
Bibliographic record
Abstract
Recently a practical and efficient off-line approach for network branch parameter (series and shunt admittances) errors detection, identification and correction was developed. In comparison with other methodologies already proposed for branch parameters validation, the novel features of this off-line approach are the identification procedure of suspicious branches and the way the augmented state-parameter estimation problem is solved. Several simulation results (with IEEE bus systems) have demonstrated the high correctness and reliability of that off-line approach to deal with single and multiple parameter errors in adjacent (those having a terminal bus in common) and non-adjacent branches. Moreover the approach was also demonstrated on tests performed on the Hydro-Québec Trans-Énergie networks. One of these tests shows that approach enables the validation of series and shunt admittances of parallel transmission lines with series compensation. However, that offline approach does not have the desired property of distinguishing between bad data (bad analog measurements) and branch parameter errors. This paper presents a simple yet effective improvement by creating new indexes that enable the distinction between bad data and branch parameter errors, even when both appear simultaneously. Simulation results have sown the effectiveness of including the new indexes in that off-line approach.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".