Adaptive Contactor – A New Scheme to Improve Induction Motor Immunity to Voltage Sags
Bibliographic record
Abstract
One of the consequences of voltage sags is unexpected motor trips or stalls. This is caused by the simple voltage-magnitude-based tripping logic used in the motor contactors. Since a motor has momentum during a voltage sag, a deep voltage sag with a short duration does not necessarily lead to motor misoperation. Based on this reasoning, a novel motor contactor operation scheme is proposed in this paper. The proposed scheme takes into account both the magnitude and duration of a voltage sag plus the momentum of the rotor, in real-time, to decide if a motor will be tripped. The core idea of the scheme is to utilize the critical clearance time (CCT) of motors as the tripping time of AC contactors. The proposed scheme combines an offline analysis and an online calculation to minimize the computation burden in real-time operation. An improved motor parameter estimation method is also proposed to ensure the accuracy of the calculated CCT. The performance of the proposed scheme is evaluated and demonstrated through comparative case studies. Moreover, some practical issues are discussed to facilitate the real implementation.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".