Harmonics Reduction in Current Source Converters Using Fuzzy Logic
Bibliographic record
Abstract
High power ratings gate turn-off (GTO) thyristors are usually used with low switching frequency in high power applications current source converters (CSCs), i.e., rectifiers and inverters. Space vector modulation (SVM) technique for CSC is established by dividing ac-side line current cycle into six sectors. Each sector is divided into certain number of space vector (SV) cycles. SV cycle is divided into three states, two active and one zero state. SVM technique generates fifth and seventh harmonics (HD5-7) in the CSC ac-side current when operated with low switching frequencies, i.e., low number of SV cycles. Minimal reduction in HD5-7was achieved by using certain states sequence inside SV cycle, and by calculating states on times at once in the middle of each SV cycle. In this paper, fuzzy logic-dependent technique for calculating states on times in SVM-CSC is proposed. First, two straightforward techniques are discussed. One calculates each state on time from SVM equations in the middle of each state effecting time period. The other calculates all states on times from SVM equations at once when the effective state is changing from one active state to the other. Both techniques give high reduction in HD5-7. Fuzzy logic technique adjusts states on times to give the least possible HD5-7with a compromise between both straightforward techniques, fuzzy logic tune states on time calculating instants in SVM-CSC. The target for fuzzy logic is to achieve fast states on times adjustment with fixed least HD5-7in steady state for states on times. States on time's adjustment with fuzzy logic is applied also to a CSC with a small size ac-side filter. An experimental investigation is introduced.
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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".