Space Vector Modulation for Low Switching Frequency Current Source Converters With Reduced Low-Order Noncharacteristic Harmonics
Bibliographic record
Abstract
High-power current source converters (CSCs) are usually implemented with gate turn off thyristors (GTOs) or integrated gate commutated thyristors (IGCTs) that present a maximum switching frequency of a few kilohertz. Space vector modulation (SVM) offers a very elegant way of generating CSC gating signals online with increased gain and reduced switching frequency. However, for very low switching frequency, SVM results in low-order (5th and 7th) noncharacteristic harmonics complicating the design of the input filter. The reduction of the magnitude of these harmonics has been sought mostly through new sequences of space vectors (states) that present better performance for different ranges of modulation index and power factor. Moderate improvement can be obtained by calculating the statespsila on times for the reference vector in the middle of an SVM cycle. This paper proposes calculating the statespsila on times as the reference vector rotates. Simulation results show that this approach results in a significant reduction in the harmonic distortion of these two components, which, for a selected sequence of states, can be limited to 0.3% of the fundamental component as the modulation index varies from 0.05 to 1.0. Experimental results obtained with a digital signal processor development kit are also provided to show the superior performance of the proposed techniques.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".