Embedded digital protection for IPMSM drives
Bibliographic record
Abstract
This paper presents the development, real-time implementation, and experimental testing of a new embedded digital protection for three phase (3φ) interior permanent magnet synchronous motor (IPMSM) drives. The proposed digital protection is established through extracting the high frequency sub-band contents present in the d-q-axis components of the currents drawn by an IPMSM. the desired high frequency sub-band contents are extracted using the wavelet packet transform (WPT). These extracted high frequency sub-band contents contain signature information that allow detecting and classifying transient disturbances occurring in IPMSM drives. Such a structure of the d - q WPT-based digital protection simplifies its implementation, and facilitates its embedding within the controller of an IPMSM drive. Performances of the proposed digital protection are investigated experimentally on a laboratory 5-hp IPMSM drive. Experimental test results for various fault and non-fault transient disturbances demonstrate fast and accurate detection, classification, and responses. Performance features, along with simple implementation of the d - q WPT-based digital protection support its application in IPMSM drives.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".