A Wind Turbine Generator Design and Optimization for DC Collector Grids
Bibliographic record
Abstract
This article presents the design and optimization of a multiphase doubly excited generator (DEG) for wind turbine applications in a dc grid. The DEG has two rotors: 1) a wound field excited (WFe) rotor, which in principals is similar to a conventional synchronous generator (SG) and 2) a surface-mounted permanent magnet excited (PMe) rotor that has a similar operation as a PM generator. The DEG is connected to a multileg passive rectifier whose output is connected to a dc wind grid. The dual rotor topology allows modification of the output power and voltage of the DEG while eliminating the need for an active power electronic converter. The DEG is parametrized and undergone a multiobjective optimization solved by employing a differential evolution algorithm (DEA). The DEG output power, mass, and efficiency are optimized subject to a list of prescribed constraints. To verify the design procedures a small-scale DEG is built and tested in the laboratory, whose results are presented in this article.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".