Coupled Magnetic Circuit-Based Design of an IPMSM for Reduction of Circulating Currents in Asymmetrical Star–Delta Windings
Bibliographic record
Abstract
Combined star (Y)–delta (Δ) windings suffer from undesirable circulating currents resulting in increased winding losses and saturation leading to demagnetization in permanent magnet synchronous machines (PMSMs). The main causes for these currents are the induced voltage harmonics from the winding configuration and rotor saliency. Thus, this paper presents a novel coupled winding function and magnetic circuit model to accurately model the winding harmonics and rotor saliency in Y–Δ wound PMSMs. Unlike existing winding harmonic analysis methods such as winding factor approach and star of slots, the developed winding function incorporates the effect of winding asymmetry. Although asymmetrical Y–Δ windings suffer from higher circulating currents, such configurations result in higher torque, efficiency and reduced torque ripple when compared to conventional symmetrical windings. Therefore, using the proposed magnetic circuit model incorporating rotor saliency, an interior PMSM (IPMSM) rotor structure is developed for reduced circulating currents without compromising the traction performance. Experimental results are presented to highlight reduced circulating currents in terms of induced voltage harmonics, machine saliency and to illustrate its effect on the machine’s superior traction performance capability such as improved rated torque, efficiency, and reduced torque ripple, winding losses and magnetic saturation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".