Elimination of low-order harmonics using a modified SHE-PWM technique for medium voltage induction motor applications
Bibliographic record
Abstract
In high power applications the inverter of a motor drive should have a switching frequency as low as possible in order to reduce the switching and snubber losses. An effective pulse width modulation (PWM) approach that can be utilized successfully with high control accuracy is combination of selective harmonic elimination and pulse width modulation (SHEPWM). This technique offers many advantages other PWM techniques including direct control over output waveform harmonics, and the ability to neglect triplen harmonics in three-phase inverter systems. SHEPWM can optimize PWM output waveforms for selected harmonic elimination or to minimize total harmonic distortion (THD). This technique is used with voltage source inverters (VSI) and current source inverters (CSI) as well. This paper presents a novel technique that uses one inverter (the reference inverter) utilizing a conventional SHE-PWM scheme to eliminate a number of specific low order harmonics in the induction motor load. The first inverter directly feeds the induction motor load. The second inverter is phase shifted with a pre-calculated angle to eliminate the first significant surplus harmonic. This set of two inverters is connected to a second similar set of two inverters, with a second pre-calculated phase shift, to eliminate the second significant surplus harmonic and so on. Harmonic currents profiles using MATLAB and PSpice simulation are compared for a three-phase high-power medium voltage induction motor load, with and without a phase-shifting transformer.
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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) | 0.002 | 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".