Performance Analysis of a Self-excited Induction Generator Mathematical Dynamic Model with Magnetic Saturation, Cross Saturation Effect and Iron Losses
Bibliographic record
Abstract
Construction of appropriate mathematical model of plant (object to be modeled) has especially important meaning for designing controller. A reliable and good control performance requires more detailed model. Nevertheless, the latter must be established from at least two viewpoints of preciseness and compactness. In this paper, the considered plant is a self-excited induction generator (SEIG). In order to improve the accuracy, we take into consideration in the SEIG modeling: magnetic saturation phenomenon, cross saturation effect and iron losses. To our best knowledge, this is the first time that the three mentioned phenomena have been integrated into the same and single SEIG model which is presented in an inherent mathematical form. As we will see in the results, the contribution of these three phenomena in the accuracy improvement is really significant. In addition, to achieve compactness of the proposed model, a simpler configuration is obtained for the electrical equivalent circuit associated to the studied model by using Thevenin transformation. The proposed model is built in MATLAB-SIMULINK environment and used to study and analyze the performance of a SEIG under various operating point conditions. The obtained results are compared to measurements and also to the values obtained from models without the three discussed phenomena.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".