Dual Reference Frame Based Current Harmonic Minimization for Dual Three-Phase PMSM Considering Inverter Voltage Limit
Bibliographic record
Abstract
This article proposes an optimized current harmonic minimization (CHM) approach for dual three-phase permanent magnet synchronous machines (PMSMs) with consideration of inverter voltage limit. The current harmonic model is derived to analyze the dominant current harmonic components in dual three-phase PMSM drives. Dual reference frame (DRF) model is proposed to convert the current harmonics into dc components in the new DRFs, and PI controllers are employed to control voltages to minimize the dc components. This article will prove that current harmonics can be minimized by using the DRF model. Since CHM requires additional voltages, inverter voltage limit must be considered especially at high speeds. Hence, inverter voltage limit is considered to derive the theoretical control strategy, in which minimal copper loss is selected as the design objective to reduce current harmonic with limited voltage. The proposed approach is supported by theoretical analysis and proof, and it does not require inverter voltage and machine parameters. Moreover, the proposed approach is compared with an existing method to show the performance improvement and evaluated with extensive tests on a laboratory prototype under both steady-state and transient conditions.
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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) | 0.001 | 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".