Comparative performance analysis of aluminum‐ and copper‐rotor induction generators in standalone wind power application
Bibliographic record
Abstract
Purpose In order to assess the performance of an induction generator in standalone wind power application, it is imperative that mathematical models are developed that accurately represent the system and take into account various electromagnetic influences such as skin effect. The purpose of this paper is to utilize mathematical models to study the transient and steady‐state behaviour of the self‐excited induction generator (SEIG), in one case with an aluminum rotor, in another case, with a copper rotor, under various load conditions while taking the above factor into account. Design/methodology/approach Mathematical models of a SEIG in the d‐q axis frame have been developed based on the generalized machine theory. A relationship between the mutual inductance and the magnetizing current of the machine has been presented. The rotor impedances have been customized to include skin effect. Using these relations, the model has been extended to include the saturation and skin effects. In order to verify the accuracy of the models, numerical and experimental investigations have been carried out on two 7.5 hp aluminum‐rotor and copper‐rotor SEIGs. Findings It was found that the model that takes into account the saturation and skin effects produces numerical results that closely match experimental values for both the machines. Originality/value This paper describes how a model of an SEIG considering saturation and skin effect has been developed and applied to aluminum‐ and copper‐rotor machines of similar power ratings to analyze their performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".