Prediction of Drive-Fed Induction Machine Efficiency Using Sine Wave Efficiency Results
Bibliographic record
Abstract
Despite their prominent capabilities and features to control the speed and torque of electrical machines, adjustable-speed drives (ASDs) reduce the efficiency of machine compared to sine-fed machines. This reduction is due to additional losses generated by harmonics in the output voltage and current of ASDs. Due to the complex nature of these losses, accurate calculation of these losses is very difficult. This paper intends to present a simple and straightforward way to predict an induction machine (IM) efficiency in drive-fed mode by using its direct-fed mode results. For this purpose, four IMs are tested in three modes: Direct-fed, Scaler drive-fed and Direct Torque Control (DTC) drive-fed. The results are compared with each other and a simple and straightforward method is proposed to predict the efficiency of a drive-fed IM using direct-fed results.
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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".