Doubly-fed induction machine for variable speed energy conversion applications
Bibliographic record
Abstract
After decades of development, the wind energy industry is now supplying 10% to 20% of power in electric utilities.At present Doubly-Fed Induction Generators (DFIG) are one of the most widely used generators in wind farms.The research of this thesis advances the methods of controlling DFIGs by presenting:(i) a non-mechanical (sensorless) method of determining accurate rotor speed and rotor position which are essential in implementing decoupled P-Q control;(ii) a method of autonomous frequency control whereby an islanded wind farm does not have to shut down but continues to operate as standby ready to assist the utility grid in fast restoration;(iii) a method of mitigating the problem of power imbalance at the initial period of islanding by using pitch control to spill excess wind power.The thesis also examines what economical adaptation is required to make the Doubly-Fed Induction Generator, which has the advanced controllers designed for wind power application, marketable as Doubly-Fed Induction Motor.Research is based on theoretical analysis, validated by digital simulation.A prototype DFIG 5hp experimental platform, which has been built and tested, provides experimental verification to claims.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".