Performance Study of a Self-Excitation Dual Stator Winding Induction Generator for Renewable Distributed Generation Systems
Bibliographic record
Abstract
Renewable power generation is a suitable technology used to deliver energy locally to customers especially in remote regions. Wind energy based on induction generator situates in a foreground position in the total energy produced using renewable sources. In the last few decades, a new self- excitation generator was based on multi-stator induction strongly emerges. This article presents a systematic modelling, a detailed analysis and the performance analysis of self-excitation dual stator winding induction generator (SE-DSWIG). The modelling of the SE-DSWIG was done with taking in account the common mutual leakage inductance between stators and the magnetizing inductance, which played a principal role in the stabilization of the output voltage in the steady state. The generator feeds the end user emulated by an inductive-resistive load. In order to simulate the weather conditions’ variation, a step change of the prime mover speed was applied on the SE-DSWIG. A passive series and shunt compensator was used to mitigate the voltage sag and swell appeared in the power system due to wind variation and the lack of reactive power consumed by the inductive load.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".