Advanced Terminal Voltage Control of Self-Excited Induction Generators in Variable-Speed Wind Turbines Using a Three-Level NPC Converter
Bibliographic record
Abstract
This study aims to improve the terminal voltage control of a self-excited induction generator (SEIG) that operates an independent load and is supplied by a wind turbine with variable speed.A three-level neutral point clamped (3L_NPC) converter employing direct torque control (DTC) is utilized to achieve this control.Two strategies are implemented: In the first strategy, the flux is maintained constant, while in the second, the flux varies with the speed.Voltage space vector selection is used to control the electromagnetic torque, stator flux, and induction generator, aiming to reduce torque and flux ripples.The three-level converter, as opposed to the two-level version, offers an increased degree of freedom in voltage vector selection, resulting in enhanced performance.The control strategy being suggested seeks to maintain a consistent voltage level across the DC bus, irrespective of fluctuations in load and wind speed one can effectively regulate the system, by controlling the torque according to the speed.A dynamic model accounting for the saturation effect of magnetic material is developed in the (α-β) frame using the Concordia transform.The effectiveness of the proposed control strategy is validated through simulation tests conducted in Matlab/Simulink.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".