Open-Phase Fault Modeling for Dual Three-Phase PMSM Using Vector Space Decomposition and Negative Sequence Components
Bibliographic record
Abstract
Post-fault modeling for dual three-phase permanent magnet synchronous motors (DTP-PMSMs) under the open-phase fault is presented in this article. By using the positive and negative sequence components for the dual three-phase system, the stator currents under the open-phase fault can be treated as a combination of the positive and negative sequence currents. By artfully constructing the negative sequence components in terms of the different initial angles and amplitudes, the single-phase open-circuit fault and certain dual-phase open-circuit fault can be represented. Based on the conventional and modified vector space decomposition (VSD), only the positive sequence components are projected into the$\alpha \beta $subspace, namely, the torque-producing subspace, and the negative sequence components are transformed into the harmonic subspace. With the conventional proportional–integral (PI) current regulators, the dc components of the positive and negative sequence currents can be readily controlled under the open-phase fault. Experiments based on a DTP-PMSM prototype verify the effectiveness of the proposed open-phase fault modeling using VSD and negative sequence components.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".