Interturn Fault Diagnosis in Sensorless PMSM Drive Based on Negative-Sequence Reactive Power Distortions
Bibliographic record
Abstract
Stator interturn fault (ITF) is the most common failure in electrical machines; if no prompt detection is implemented, it can cause catastrophic results. This work proposes a novel method in permanent magnet synchronous machine (PMSM) drives to detect the ITF, which is insular to speed and load variations. The proposed ITF technique is based on negative-sequence instantaneous reactive power (IRP) distortions. The sensorless control of the PMSM drive, while using field-oriented technique, uses the voltage and current information for rotor position estimation. This serves the dual purpose of controlling the drive and also in developing the diagnostic technique. The IRP distortion is calculated from dq-reference frame voltage distortions, which are estimated using Luenberger observer and dq-reference frame current distortions. The novel fault indicator is calculated based on the vector magnitude of dc components obtained from negative-sequence IRP distortions, which is insular to various speed and load conditions of the drive. The proposed ITF detection technique is experimentally validated under varying load and speed conditions of the sensorless field-oriented controlled (FOC) PMSM drive. Further, a comparison of the proposed ITF detection scheme with the dq-reference frame current residuals technique shows the superiority of the proposed ITF detection scheme under various speed and load conditions of the PMSM drive scheme; furthermore, the reliability of the proposed ITF detection technique under various noise conditions is also verified.
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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 | 0.000 |
| 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 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".