Independent Phase Current Control of a Permanent Magnet Motor
Bibliographic record
Abstract
Open-winding permanent magnet (PM) motors are gaining popularity in transportation applications due to several advantages over traditional PM motors, such as a wider speed range, and better fault tolerance. This paper presents a current control technique wherein the individual harmonics of the winding current, resulting due to the harmonics in the machine back-EMF waveform, are controlled. Multiple harmonic controllers are used on different harmonic reference frames to ensure control of the corresponding harmonic currents. Real-time simulations are first presented to show the dynamic performance of the machine with the proposed current control technique for a wide variety of transient conditions such as machine start-up and fault operation with only two phases energized. To further validate the utility of the proposed current control technique, a torque-ripple minimization algorithm available in the literature, is used to generate current references for the proposed current controller. Real-time simulations are also presented for the torque-ripple minimization algorithm implementation along with the proposed current controller. This is followed by experimental implementation of the proposed controller. In the experimental implementation high bandwidth linear amplifiers are used to control the winding currents of a physical open-winding PM 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".