Automatic Inductance Measurements of Synchronous Reluctance Machines Including Cross-Saturation Using Real-Time Systems
Bibliographic record
Abstract
This paper presents a new approach to the inductance measurement of a synchronous reluctance machine. Instead of an open loop pulsed voltage reference described in many references, a pulsed current reference is applied to the test machine in closed loop. The response of the machine is measured, and voltage and current samples are processed (integration) in real time to calculate the flux linkages and inductances. Since the measurement is done in current control mode, the time constant of the overall system can be modified in such a way that a sufficient number of samples are available during the transient even for machines with smaller time constants. This improves the accuracy of the measurement for low time constant machines compared to the voltage reference based measurement. Additionally, inductance at a desired operating point is obtained in a single trial; this reduces the duration of the measurement process. Unlike the pulsed voltage reference method that requires post processing of a huge data set to calculate the inductance map of the machine, the proposed method can be programmed in a real time processor to automate the generation of an inductance map of the test machine.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".