Bibliographic record
Abstract
The technique of calculating the air gap torque of an induction motor utilizing only the terminal voltages and currents has been known for some time; however implementation of this technique in a commercial instrument that must compete in accuracy with electromechanical instruments raises a number of issues that must be resolved. Because the derivation of the theorem relies on assumptions of symmetry in the motor, questions have been raised as to its accuracy in real machines. Also, the effects of saturation and core losses usually are ignored but are always present, especially in '' commodity''-type motors. There also are issues associated with the way data are processed, especially in the presence of strong harmonics. Finally, there is the question of how the system can be put into the field using only the information available to the user on the motor name plate. To clarify these issues, an experiment was set up using a PC equipped with standard A/D boards and a common 3/4 HP induction motor. The algorithm was implemented in C code on the PC. Upon the conclusion of these tests, the code was ported to a switchboard instrument and tested using a 15 HP motor. Results in both platforms were good, and the switchboard implementation has been adopted into the standard motor protection relay product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".