Effective Model Predictive Voltage Control for a Sensorless Doubly Fed Induction Generator
Bibliographic record
Abstract
This article presents a novel model predictive voltage control (MP VC) for a doubly fed induction generator (DFIG) without a speed sensor. The methodology of the considered MP VC is articulated on the direct voltage control by incorporating the deadbeat control principle within the model predictive topology. The derivation of the utilized cost function is accomplished in organized steps. The finite control set (FCS) principle is adopted to avoid the utilization of the pulsewidth modulation (PWM), which contributes to simplifying the system configuration. For estimating the rotor position, a robust estimator is proposed to achieve precise tracking of the rotor alignment, and thus, a perfect co-ordinates transformation can be achieved. To visualize the significance of the intended MP VC in regard to the classic model predictive techniques, accurate analysis of the DFIG dynamics under the proposed MP VC and model predictive direct torque control (MP DTC) is presented. The test results approve and reveal the predomination of the presented MP VC over the MP DTC. Furthermore, the effectiveness of the proposed sensorless scheme is verified for different ranges of speed operation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".