Open-Phase Fault Modeling and Optimized Fault-Tolerant Control of Dual Three-Phase Permanent Magnet Synchronous Machines
Bibliographic record
Abstract
This paper investigates open-phase fault modeling and fault-tolerant control (FTC) of dual three-phase permanent magnet synchronous machines (DT-PMSMs). A comprehensive fault model that considers both the permanent magnet torque and reluctance torque under the open-phase fault is proposed first. This model shows that under open-phase fault the average torque of DT-PMSM will decrease, while torque ripple will increase significantly. Then, a novel optimized FTC approach is developed based on the proposed model, in which genetic algorithm (GA) is applied to optimize the stator currents to maximize the average torque and minimize the torque ripple under open-phase fault. The proposed fault model and GA-based FTC are applicable to both surface-mounted and interior DT-PMSMs. However, existing approaches neglecting the reluctance torque are only applicable to surfaced-mounted DT-PMSMs. Moreover, the proposed approach is simple in implementation as it employs the original control structure and it is capable of smooth switching between the healthy operation and FTC without inducing noticeable torque pulses. The proposed approach is demonstrated with design examples, compared with existing one and validated with experiments on a laboratory interior DT-PMSM.
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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) | 0.000 | 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".