Comparison of Two Methods for Full-Load In Situ Induction Motor Efficiency Estimation From Field Testing in the Presence of Over/Undervoltages and Unbalanced Supplies
Bibliographic record
Abstract
Numerous techniques with different levels of intrusion and accuracy have been proposed for in situ efficiency estimation. Among them, optimization-based techniques and the air-gap torque (AGT) method show promise when unbalanced supply conditions exist. In this paper, an optimization-based algorithm is proposed for in situ efficiency estimation of induction machines operating with over-/undervoltage and unbalanced supplies. In addition, a comprehensive study is done on the functionality and accuracy of the nonintrusive AGT method which is claimed to be one of the most promising methods in the literature. It is shown that the efficiency calculated by this method under field conditions cannot be used in the decision making process on replacement of the existing machines as well as the relevant calculations regarding the payback period. The research is supported by experimental results on two different induction machines.
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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 | 0.001 |
| 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".