Power estimation of induction generators fed from wind turbines
Bibliographic record
Abstract
Wind energy has good potential for reducing the environmental impact and cost of electricity generation in diesel based stand-alone systems (mini-grids). However, due to the stochastic nature of wind and the highly variable loads typical of remote communities, the diesel power plants can be subject to significant variation in the power demand. This can create large variations in the grid frequency and also make the diesel gensets operate at low load what can lead to carbon build up in the engines and increased maintenance costs. Some power electronic based wind turbines can provide frequency support to mitigate these problems but might be too costly for operation in small mini-grids. Conversely, there is the fixed-pitch fixed-speed induction generator based (type 1) wind turbine which does not allow any active control but presents low capital and maintenance costs. In this paper, an approach for obtaining the power vs. grid frequency characteristics of a type 1 wind turbine is described and validated experimentally. It shows that the power injected into the grid by a type 1 wind turbine reduces as the grid frequency increases. Therefore, it can help with frequency regulation in a passive way.
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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 | 0.002 |
| 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 | 0.000 | 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".