Efficiency Comparison of Power Electronic Converters Used in Grid-Connected Permanent-Magnet Wind Energy Conversion System Based on Semiconductors Power Losses
Bibliographic record
Abstract
An efficiency comparison of power converters is presented for a permanent magnet generator based grid-connected wind energy conversion system. The power converters examined are: the intermediate boost converter (IBC), the intermediate buck-boost converter (IBBC), the back-to-back converter (BBC) and the matrix converter (MC). The aim is to determine which power electronic converter yields the highest efficiency in terms of power losses of the semiconductor devices with the varying wind speeds. In view of this, a furled wind turbine model is developed that generates power for different wind speeds. Afterwards, a relation between the wind speed and power loss is established to evaluate the efficiency of the power electronic converters for discrete wind speeds. The power loss model presented in this paper has taken into account the conduction and switching losses of the semiconductor devices within each converter. Simulation results are presented showing the power loss characteristics with the variation in wind speeds. Finally, with regard to the global efficiency for the power electronic converters of the considered wind speed regime, the IBC is found to be the most favourable choice considering the typical wind conditions encountered by a wind energy conversion system operating at the kWatt level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".