Development and Performance Testing of a $V\!\!/\!f$ Control for Permanent Magnet Synchronous Motor Drives With Wavelet Modulated Power Electronic Converters
Bibliographic record
Abstract
Volt-per-hertz ($V/f$) control is one the simplest control structures developed for operating electric motor drives, including permanent magnet synchronous motor (PMSM) drives. This paper presents the development and testing of a$V/f$control for PMSM drives that utilize a$3\phi$wavelet modulated (WM) dc-ac power electronic converter (PEC). The wavelet modulation technique is characterized by the maximum scale ($J$) and scale-time interval factor ($\gamma$). The output voltage of a$3\phi$WM dc-ac PEC has its magnitude directly dependent on$J$, while its frequency and phase are dependent to$\gamma$. These two parameters of the wavelet modulation technique can be used to implement a$V/f$control for PMSM drives. The proposed$V/f$control is featured with a stabilizing loop to correct the$V/f$ratio, and improve the stability of the controlled PMSM drive. The$V/f$control for a PMSM drive fed by a wavelet modulated dc-ac PEC, is implemented for performance testing using a laboratory 10$-hp$PMSM drive under different operating conditions. Performance results demonstrate stable, dynamic, and accurate responses, with minor sensitivities to the load torque and/or speed changes. Furthermore, test results demonstrate the ability to enhance the efficiency using the tested control for PMSM drives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".