Bibliographic record
Abstract
This paper provides a simple but accurate method for calculating switching harmonics generated by the pulse width modulation (PWM). This method can be used to design filter for converters and to evaluate EMI impacts. Three-phase PWM inverters have been wildly used as major power stages for a three-phase dc-ac power conversions for many years. The maturation of techniques on this type of inverter has expanded its applications from the general industry to the aerospace. One of the side effects of this type of inverters is the switching harmonics generated by the pulse-width modulation (PWM). Many papers were published regarding the harmonics analysis. However, most of them were focusing on the output ac voltages only. In addition, complex mathematics used in the analysis prevents the practical engineers from understanding and using them in the inverter design. This paper uses a simple mathematical method for calculating switching harmonics in the output voltage, as well as the switching harmonics in the dc link. The paper will introduce the relationship between the harmonic spectrum and the mathematic equation associated with individual harmonic components. The distribution of individual switching harmonic on the harmonic spectrum is illustrated. The close form equations for calculating each individual harmonics are provided. With this method, the power electronics designer can calculate the harmonics before the converters are built and tested. The proper filters can be designed based the harmonics calculation and the EMI effects can also be addressed at the same time. The method is verified by simulation using different simulation tools. It is also validated by practical measurement in a high power converter application to be appreciated for use in the inverter design.
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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".